Install packages needed.
First, create a vector of all the packages needed:
packages <- c("agricolae", "dplyr", "plyr", "ggplot2", "readr", "ggpubr", "car",
"rcompanion", "tidyverse", "ggsignif", "reshape", "rmdformats")Install packages not yet installed
installed_packages <- packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {
install.packages(packages[!installed_packages])
}Load all packages
invisible(lapply(packages, library, character.only = TRUE))## Warning: package 'dplyr' was built under R version 4.0.5##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union## ------------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## ------------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following objects are masked from 'package:dplyr':
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## arrange, count, desc, failwith, id, mutate, rename, summarise,
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## recode## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ tibble 3.1.6 ✓ stringr 1.4.0
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## Attaching package: 'reshape'## The following objects are masked from 'package:tidyr':
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## expand, smiths## The following objects are masked from 'package:plyr':
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## renameSet the working directory to the directory where the output files will be saved.
In this example, we assume you have cloned/donwloaded this repository to your “Documents” folder.
Change directory on mac/linux:
setwd(“/Users/YOURUSERNAME/Documents/X.necrophora.secondaryMetabolites/output”)
Change directory on Windows (Windows 10 in this example):
setwd(“C:/Users/YOURUSERNAME/Documents/X.necrophora.secondaryMetabolites/output”)
For this demosntration, we did not export the files in PDF to the output directory. If you wish to do so, do the following:
Step 1: Call the pdf command to start the plot
pdf(file = "/Users/YOURUSERNAME/Documents/X.necrophora.secondaryMetabolites/output/
Figure1.pdf", # The directory you want to save the file in
width = 7, # The width of the plot in inches
height = 5) # The height of the plot in inchesStep 2: Add the code provided below for your desired plot.
Step 3: Run dev.off() to create the file!
dev.off()For this example, we set the working directory to the following:
setwd("/Users/tedggarcia/Documents/Xylaria.Mycotoxins.2020/Manuscript/Submission/GitHubRepository/X.necrophora.secondaryMetabolites/output/")Loading digital chlorophyll content datasets (only one repetition of each experiment for illustration purposes). All datasets can be found in the folder named “raw_data”
ES2 = First experiment for 14 Days of exporuse (DOE)
#ES4 = Repetetion for 14 DOE
ES5 = First experiment for 7 DOE
#ES8 = Repetition for 7 DOE
#ES13A = Experiment testing potentially resistant cultivars (7DOE)
ES13B = Repetition of ES13A
ES14A = Experiment testing effects among plant species (7DOE)
#ES14B = Repetition of ES14A
ES2 <- read.csv("../raw_data/ES2.ChlorophyllContent.14DOE.Exp1.csv", header = T)
ES5 <- read.csv("../raw_data/ES5.ChlorophyllContent.7DOE.Exp1.csv", header = T)
ES13B <- read.csv("../raw_data/ES13B.ChlorophyllContent.7DOE.Exp2.Cultivars.csv",
header = T)
ES14A <- read.csv("../raw_data/ES14A.ChlorophyllContent.7DOE.Exp1.PlantSpecies.csv",
header = T)Run Shapiro-Wilk Tests to check for normality
shapiro.test(ES2$chl)##
## Shapiro-Wilk normality test
##
## data: ES2$chl
## W = 0.74674, p-value < 2.2e-16shapiro.test(ES5$chl)##
## Shapiro-Wilk normality test
##
## data: ES5$chl
## W = 0.95514, p-value = 5.341e-10shapiro.test(ES13B$chl)##
## Shapiro-Wilk normality test
##
## data: ES13B$chl
## W = 0.95496, p-value = 2.7e-07shapiro.test(ES14A$chl)##
## Shapiro-Wilk normality test
##
## data: ES14A$chl
## W = 0.95203, p-value = 1.513e-06Check the distribution of the data and assess if normalization is needed.
ggdensity(ES2$chl, main = "Density of Chlorophyll Content (digital) for ES2",
xlab = "Datapoints")## Warning: Removed 60 rows containing non-finite values (stat_density).
ggdensity(ES5$chl, main = "Density of Chlorophyll Content (digital) for ES5",
xlab = "Datapoints")## Warning: Removed 12 rows containing non-finite values (stat_density).
ggdensity(ES13B$chl, main = "Density of Chlorophyll Content (digital) for ES13B",
xlab = "Datapoints")## Warning: Removed 6 rows containing non-finite values (stat_density).
ggdensity(ES14A$chl, main = "Density of Chlorophyll Content (digital) for ES14A",
xlab = "Datapoints")## Warning: Removed 3 rows containing non-finite values (stat_density).
ggqqplot(ES2$chl)## Warning: Removed 60 rows containing non-finite values (stat_qq).## Warning: Removed 60 rows containing non-finite values (stat_qq_line).
## Removed 60 rows containing non-finite values (stat_qq_line).
ggqqplot(ES5$chl)## Warning: Removed 12 rows containing non-finite values (stat_qq).## Warning: Removed 12 rows containing non-finite values (stat_qq_line).
## Removed 12 rows containing non-finite values (stat_qq_line).
ggqqplot(ES13B$chl)## Warning: Removed 6 rows containing non-finite values (stat_qq).## Warning: Removed 6 rows containing non-finite values (stat_qq_line).
## Removed 6 rows containing non-finite values (stat_qq_line).
ggqqplot(ES14A$chl)## Warning: Removed 3 rows containing non-finite values (stat_qq).## Warning: Removed 3 rows containing non-finite values (stat_qq_line).
## Removed 3 rows containing non-finite values (stat_qq_line).
plotNormalHistogram(ES2$chl, main = "Density of Chlorophyll Content (Digital) for ES2",
xlab = "Datapoints")
plotNormalHistogram(ES5$chl, main = "Density of Chlorophyll Content (Digital) for ES5",
xlab = "Datapoints")
plotNormalHistogram(ES13B$chl, main = "Density of Chlorophyll Content (Digital) for E13B",
xlab = "Datapoints")
plotNormalHistogram(ES14A$chl, main = "Density of Chlorophyll Content (Digital) for E14A",
xlab = "Datapoints")
Use the Tukey’s tranformation method to normalize the distribution and append to datasets
ES2_chl.tuk = transformTukey(ES2$chl, plotit=FALSE)##
## lambda W Shapiro.p.value
## 416 0.375 0.9449 3.664e-09
##
## if (lambda > 0){TRANS = x ^ lambda}
## if (lambda == 0){TRANS = log(x)}
## if (lambda < 0){TRANS = -1 * x ^ lambda}ES5_chl.tuk = transformTukey(ES5$chl, plotit=FALSE)##
## lambda W Shapiro.p.value
## 427 0.65 0.9695 1.098e-07
##
## if (lambda > 0){TRANS = x ^ lambda}
## if (lambda == 0){TRANS = log(x)}
## if (lambda < 0){TRANS = -1 * x ^ lambda}ES13B_chl.tuk = transformTukey(ES13B$chl, plotit=FALSE)##
## lambda W Shapiro.p.value
## 432 0.775 0.9604 1.226e-06
##
## if (lambda > 0){TRANS = x ^ lambda}
## if (lambda == 0){TRANS = log(x)}
## if (lambda < 0){TRANS = -1 * x ^ lambda}ES14A_chl.tuk = transformTukey(ES14A$chl, plotit=FALSE)##
## lambda W Shapiro.p.value
## 470 1.725 0.979 0.00282
##
## if (lambda > 0){TRANS = x ^ lambda}
## if (lambda == 0){TRANS = log(x)}
## if (lambda < 0){TRANS = -1 * x ^ lambda}Append the transformed values to original datasets
ES2.mod <- cbind(ES2, ES2_chl.tuk)
ES5.mod <- cbind(ES5, ES5_chl.tuk)
ES13B.mod <- cbind(ES13B, ES13B_chl.tuk)
ES14A.mod <- cbind(ES14A, ES14A_chl.tuk)Statistical analyses
Run ANOVA and Tukey’s honest significance differences for raw chlorophyll content.
ES2 dataset (untransformed data)
As desribed above, this experiment was ran using cell-free culture filtrates (CFCFs) from three local strains of Xylaria necrophora (DMCC2126, DMCC2127, and DMCC2165) and one Colletotrichum siamense (DMCC2966) for 14 days (ES2)
##Extract all control (ES2), colletrichum, and X. necrophora
ES2.control <- subset(ES2.mod, Treatment== "control")
ES2.coll <- subset(ES2.mod, Treatment== "DMCC2966")
ES2.Xn <- subset(ES2.mod, Treatment== c("DMCC2126", "DMCC2127", "DMCC2165"))
ES2.control <- ES2.control %>%
add_column(Species = "control")
ES2.coll <- ES2.coll %>%
add_column(Species = "C.siamense")
ES2.Xn <- ES2.Xn %>%
add_column(Species = "X.necrophora")
ES2.mod.v2 <- rbind(ES2.control, ES2.coll, ES2.Xn)
#Run ANOVA
ES2.mod.v2.lm <- lm (ES2.mod.v2$chl ~ ES2.mod.v2$Species +
ES2.mod.v2$Dilution +
ES2.mod.v2$Condition +
ES2.mod.v2$isoRep +
ES2.mod.v2$techRep +
ES2.mod.v2$sampleNumber)
ES2.mod.v2.lm##
## Call:
## lm(formula = ES2.mod.v2$chl ~ ES2.mod.v2$Species + ES2.mod.v2$Dilution +
## ES2.mod.v2$Condition + ES2.mod.v2$isoRep + ES2.mod.v2$techRep +
## ES2.mod.v2$sampleNumber)
##
## Coefficients:
## (Intercept) ES2.mod.v2$Speciescontrol
## 190.37 42.37
## ES2.mod.v2$SpeciesX.necrophora ES2.mod.v2$Dilution25fold
## -120.68 -119.47
## ES2.mod.v2$ConditionStationary ES2.mod.v2$isoRepisolateRep2
## -31.14 32.50
## ES2.mod.v2$techRepStem2 ES2.mod.v2$techRepStem3
## -27.84 49.82
## ES2.mod.v2$sampleNumbersample2 ES2.mod.v2$sampleNumbersample3
## 32.77 39.91summary(ES2.mod.v2.lm)##
## Call:
## lm(formula = ES2.mod.v2$chl ~ ES2.mod.v2$Species + ES2.mod.v2$Dilution +
## ES2.mod.v2$Condition + ES2.mod.v2$isoRep + ES2.mod.v2$techRep +
## ES2.mod.v2$sampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -248.96 -74.34 -0.74 59.70 514.77
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 190.37 27.55 6.910 9.90e-11 ***
## ES2.mod.v2$Speciescontrol 42.37 22.05 1.921 0.0564 .
## ES2.mod.v2$SpeciesX.necrophora -120.68 22.05 -5.474 1.60e-07 ***
## ES2.mod.v2$Dilution25fold -119.47 18.18 -6.571 6.17e-10 ***
## ES2.mod.v2$ConditionStationary -31.14 18.18 -1.713 0.0885 .
## ES2.mod.v2$isoRepisolateRep2 32.50 18.14 1.791 0.0750 .
## ES2.mod.v2$techRepStem2 -27.84 23.51 -1.184 0.2379
## ES2.mod.v2$techRepStem3 49.82 21.13 2.358 0.0196 *
## ES2.mod.v2$sampleNumbersample2 32.77 22.00 1.490 0.1381
## ES2.mod.v2$sampleNumbersample3 39.91 22.02 1.812 0.0718 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 119.5 on 166 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.4277, Adjusted R-squared: 0.3967
## F-statistic: 13.79 on 9 and 166 DF, p-value: < 2.2e-16anova(ES2.mod.v2.lm)## Analysis of Variance Table
##
## Response: ES2.mod.v2$chl
## Df Sum Sq Mean Sq F value Pr(>F)
## ES2.mod.v2$Species 2 846902 423451 29.6422 9.824e-12 ***
## ES2.mod.v2$Dilution 1 624310 624310 43.7027 4.992e-10 ***
## ES2.mod.v2$Condition 1 30212 30212 2.1149 0.147764
## ES2.mod.v2$isoRep 1 37904 37904 2.6533 0.105230
## ES2.mod.v2$techRep 2 178878 89439 6.2609 0.002391 **
## ES2.mod.v2$sampleNumber 2 54292 27146 1.9003 0.152769
## Residuals 166 2371375 14285
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##Extract all control (ES5: 7 DOE)
ES5.control <- subset(ES5.mod, Treatment== "control")
ES5.Xn <- subset(ES5.mod, Treatment== c("DMCC2126", "DMCC2127", "DMCC2165"))
ES5.control <- ES5.control %>%
add_column(Species = "control")
ES5.Xn <- ES5.Xn %>%
add_column(Species = "X.necrophora")
ES5.mod.v2 <- rbind(ES5.control, ES5.Xn)
#Run ANOVA by "species"
ES5.mod.v2.lm <- lm (ES5.mod.v2$chl ~ ES5.mod.v2$Species +
ES5.mod.v2$Dilution +
ES5.mod.v2$Condition +
ES5.mod.v2$isoRep +
ES5.mod.v2$techRep +
ES5.mod.v2$sampleNumber)
ES5.mod.v2.lm##
## Call:
## lm(formula = ES5.mod.v2$chl ~ ES5.mod.v2$Species + ES5.mod.v2$Dilution +
## ES5.mod.v2$Condition + ES5.mod.v2$isoRep + ES5.mod.v2$techRep +
## ES5.mod.v2$sampleNumber)
##
## Coefficients:
## (Intercept) ES5.mod.v2$SpeciesX.necrophora
## 204.46 -66.84
## ES5.mod.v2$Dilution25fold ES5.mod.v2$ConditionStationary
## -47.47 35.71
## ES5.mod.v2$isoRepisolateRep2 ES5.mod.v2$isoRepisolateRep3
## -25.11 -30.96
## ES5.mod.v2$techRepstemRep2 ES5.mod.v2$techRepstemRep3
## -12.06 11.26
## ES5.mod.v2$sampleNumbersample2 ES5.mod.v2$sampleNumbersample3
## 12.15 28.88summary(ES5.mod.v2.lm)##
## Call:
## lm(formula = ES5.mod.v2$chl ~ ES5.mod.v2$Species + ES5.mod.v2$Dilution +
## ES5.mod.v2$Condition + ES5.mod.v2$isoRep + ES5.mod.v2$techRep +
## ES5.mod.v2$sampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -177.109 -40.050 0.166 40.290 160.338
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 204.459 13.966 14.640 < 2e-16 ***
## ES5.mod.v2$SpeciesX.necrophora -66.837 8.708 -7.675 7.37e-13 ***
## ES5.mod.v2$Dilution25fold -47.471 8.708 -5.451 1.48e-07 ***
## ES5.mod.v2$ConditionStationary 35.714 8.708 4.101 6.00e-05 ***
## ES5.mod.v2$isoRepisolateRep2 -25.108 10.652 -2.357 0.01939 *
## ES5.mod.v2$isoRepisolateRep3 -30.963 10.606 -2.919 0.00391 **
## ES5.mod.v2$techRepstemRep2 -12.058 10.773 -1.119 0.26436
## ES5.mod.v2$techRepstemRep3 11.264 10.693 1.053 0.29345
## ES5.mod.v2$sampleNumbersample2 12.148 10.635 1.142 0.25472
## ES5.mod.v2$sampleNumbersample3 28.882 10.674 2.706 0.00741 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 62.68 on 198 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.3975, Adjusted R-squared: 0.3701
## F-statistic: 14.51 on 9 and 198 DF, p-value: < 2.2e-16anova(ES5.mod.v2.lm)## Analysis of Variance Table
##
## Response: ES5.mod.v2$chl
## Df Sum Sq Mean Sq F value Pr(>F)
## ES5.mod.v2$Species 1 235287 235287 59.8872 5.029e-13 ***
## ES5.mod.v2$Dilution 1 125107 125107 31.8433 5.725e-08 ***
## ES5.mod.v2$Condition 1 65438 65438 16.6559 6.496e-05 ***
## ES5.mod.v2$isoRep 2 39008 19504 4.9643 0.007877 **
## ES5.mod.v2$techRep 2 19340 9670 2.4612 0.087937 .
## ES5.mod.v2$sampleNumber 2 29007 14504 3.6916 0.026664 *
## Residuals 198 777909 3929
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1###############ES2 analysis (raw data)################################
ES2.chl.lm <- lm (ES2$chl ~ ES2$Treatment +
ES2$Dilution +
ES2$Condition +
ES2$isoRep +
ES2$techRep +
ES2$sampleNumber)
ES2.chl.lm##
## Call:
## lm(formula = ES2$chl ~ ES2$Treatment + ES2$Dilution + ES2$Condition +
## ES2$isoRep + ES2$techRep + ES2$sampleNumber)
##
## Coefficients:
## (Intercept) ES2$TreatmentDMCC2126 ES2$TreatmentDMCC2127
## 236.806 -140.175 -173.159
## ES2$TreatmentDMCC2165 ES2$TreatmentDMCC2966 ES2$Dilution25fold
## -169.865 -44.126 -102.848
## ES2$ConditionStationary ES2$isoRepisolateRep2 ES2$techRepStem2
## -8.823 23.729 -24.695
## ES2$techRepStem3 ES2$sampleNumbersample2 ES2$sampleNumbersample3
## 16.950 26.386 30.435summary(ES2.chl.lm)##
## Call:
## lm(formula = ES2$chl ~ ES2$Treatment + ES2$Dilution + ES2$Condition +
## ES2$isoRep + ES2$techRep + ES2$sampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -231.24 -49.47 1.55 41.40 536.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 236.806 20.365 11.628 < 2e-16 ***
## ES2$TreatmentDMCC2126 -140.175 18.956 -7.395 1.70e-12 ***
## ES2$TreatmentDMCC2127 -173.159 19.204 -9.017 < 2e-16 ***
## ES2$TreatmentDMCC2165 -169.865 18.952 -8.963 < 2e-16 ***
## ES2$TreatmentDMCC2966 -44.126 18.481 -2.388 0.0176 *
## ES2$Dilution25fold -102.848 11.998 -8.572 7.35e-16 ***
## ES2$ConditionStationary -8.823 11.944 -0.739 0.4607
## ES2$isoRepisolateRep2 23.729 11.964 1.983 0.0483 *
## ES2$techRepStem2 -24.695 15.316 -1.612 0.1080
## ES2$techRepStem3 16.950 14.020 1.209 0.2277
## ES2$sampleNumbersample2 26.386 14.436 1.828 0.0687 .
## ES2$sampleNumbersample3 30.435 14.489 2.101 0.0366 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 100.5 on 276 degrees of freedom
## (72 observations deleted due to missingness)
## Multiple R-squared: 0.4591, Adjusted R-squared: 0.4375
## F-statistic: 21.3 on 11 and 276 DF, p-value: < 2.2e-16anova(ES2.chl.lm)## Analysis of Variance Table
##
## Response: ES2$chl
## Df Sum Sq Mean Sq F value Pr(>F)
## ES2$Treatment 4 1458908 364727 36.1018 < 2.2e-16 ***
## ES2$Dilution 1 732380 732380 72.4932 1.094e-15 ***
## ES2$Condition 1 3246 3246 0.3213 0.57128
## ES2$isoRep 1 38119 38119 3.7732 0.05310 .
## ES2$techRep 2 80731 40366 3.9955 0.01947 *
## ES2$sampleNumber 2 53280 26640 2.6369 0.07338 .
## Residuals 276 2788355 10103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Tukey's HSD for Variable chl by Treament
ES2.chl.treatment.HSD.test <- HSD.test(ES2.chl.lm, 'ES2$Treatment', group = T)
ES2.chl.treatment.HSD.test## $statistics
## MSerror Df Mean CV
## 10102.73 276 105.3393 95.41771
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2$Treatment 5 3.883285 0.05
##
## $means
## ES2$chl std r Min Max Q25 Q50 Q75
## control 206.91423 217.07353 57 0 831.472 26.54900 138.046 272.67000
## DMCC2126 73.25279 74.61783 57 0 281.899 11.31300 29.554 129.60000
## DMCC2127 37.91085 49.89550 54 0 167.994 8.52575 15.327 49.05425
## DMCC2165 30.48823 45.19861 57 0 187.945 8.36200 14.000 20.43000
## DMCC2966 167.98710 89.73008 63 0 309.266 119.20850 177.714 233.30650
##
## $comparison
## NULL
##
## $groups
## ES2$chl groups
## control 206.91423 a
## DMCC2966 167.98710 a
## DMCC2126 73.25279 b
## DMCC2127 37.91085 b
## DMCC2165 30.48823 b
##
## attr(,"class")
## [1] "group"#Tukey's HSD for Variable chl by Dilution
ES2.chl.dilution.HSD.test <- HSD.test(ES2.chl.lm, 'ES2$Dilution', group = T)
ES2.chl.dilution.HSD.test## $statistics
## MSerror Df Mean CV
## 10102.73 276 105.3393 95.41771
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2$Dilution 2 2.784016 0.05
##
## $means
## ES2$chl std r Min Max Q25 Q50 Q75
## 100fold 157.13270 159.97363 138 0 831.472 36.10000 129.1440 206.71875
## 25fold 57.68939 79.35162 150 0 309.266 9.85425 15.6685 99.75575
##
## $comparison
## NULL
##
## $groups
## ES2$chl groups
## 100fold 157.13270 a
## 25fold 57.68939 b
##
## attr(,"class")
## [1] "group"#Complete ANOVA for ES2 by treatment by dilution
ES2.comp.HSD.group <- HSD.test(ES2.chl.lm, c("ES2$Treatment", "ES2$Dilution"), group=TRUE,console=TRUE)##
## Study: ES2.chl.lm ~ c("ES2$Treatment", "ES2$Dilution")
##
## HSD Test for ES2$chl
##
## Mean Square Error: 10102.73
##
## ES2$Treatment:ES2$Dilution, means
##
## ES2.chl std r Min Max
## control:100fold 383.864000 223.675014 24 97.748 831.472
## control:25fold 78.223485 77.070835 33 0.000 268.776
## DMCC2126:100fold 127.480933 64.977439 30 10.433 281.899
## DMCC2126:25fold 12.999296 10.944223 27 0.000 51.676
## DMCC2127:100fold 58.980593 59.597226 27 0.000 167.994
## DMCC2127:25fold 16.841111 24.515869 27 0.000 112.319
## DMCC2165:100fold 58.801375 58.889805 24 0.000 187.945
## DMCC2165:25fold 9.896848 6.632284 33 0.000 19.414
## DMCC2966:100fold 171.013333 97.165275 33 0.000 301.867
## DMCC2966:25fold 164.658233 82.303611 30 0.000 309.266
##
## Alpha: 0.05 ; DF Error: 276
## Critical Value of Studentized Range: 4.511094
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ES2$chl groups
## control:100fold 383.864000 a
## DMCC2966:100fold 171.013333 b
## DMCC2966:25fold 164.658233 b
## DMCC2126:100fold 127.480933 bc
## control:25fold 78.223485 cd
## DMCC2127:100fold 58.980593 cd
## DMCC2165:100fold 58.801375 cd
## DMCC2127:25fold 16.841111 d
## DMCC2126:25fold 12.999296 d
## DMCC2165:25fold 9.896848 dES2.comp.HSD.group## $statistics
## MSerror Df Mean CV
## 10102.73 276 105.3393 95.41771
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2$Treatment:ES2$Dilution 10 4.511094 0.05
##
## $means
## ES2$chl std r Min Max Q25 Q50
## control:100fold 383.864000 223.675014 24 97.748 831.472 244.69000 280.5385
## control:25fold 78.223485 77.070835 33 0.000 268.776 15.68300 59.4900
## DMCC2126:100fold 127.480933 64.977439 30 10.433 281.899 81.64425 129.1440
## DMCC2126:25fold 12.999296 10.944223 27 0.000 51.676 9.86550 11.3130
## DMCC2127:100fold 58.980593 59.597226 27 0.000 167.994 12.11000 35.6240
## DMCC2127:25fold 16.841111 24.515869 27 0.000 112.319 0.00000 11.9040
## DMCC2165:100fold 58.801375 58.889805 24 0.000 187.945 14.21225 25.3885
## DMCC2165:25fold 9.896848 6.632284 33 0.000 19.414 0.00000 12.2830
## DMCC2966:100fold 171.013333 97.165275 33 0.000 301.867 118.40500 176.8540
## DMCC2966:25fold 164.658233 82.303611 30 0.000 309.266 120.78250 181.5795
## Q75
## control:100fold 527.0058
## control:25fold 129.7670
## DMCC2126:100fold 159.8775
## DMCC2126:25fold 16.5335
## DMCC2127:100fold 90.5650
## DMCC2127:25fold 15.6860
## DMCC2165:100fold 105.9032
## DMCC2165:25fold 14.7740
## DMCC2966:100fold 241.9460
## DMCC2966:25fold 222.5877
##
## $comparison
## NULL
##
## $groups
## ES2$chl groups
## control:100fold 383.864000 a
## DMCC2966:100fold 171.013333 b
## DMCC2966:25fold 164.658233 b
## DMCC2126:100fold 127.480933 bc
## control:25fold 78.223485 cd
## DMCC2127:100fold 58.980593 cd
## DMCC2165:100fold 58.801375 cd
## DMCC2127:25fold 16.841111 d
## DMCC2126:25fold 12.999296 d
## DMCC2165:25fold 9.896848 d
##
## attr(,"class")
## [1] "group"Same analysis using the normalized dataset
###############ES2 analysis (normalized dataset) ################################
ES2.mod.chl.lm <- lm (ES2.mod$ES2_chl.tuk ~ ES2.mod$Treatment +
ES2.mod$Dilution +
ES2.mod$Condition +
ES2.mod$isoRep +
ES2.mod$techRep +
ES2.mod$sampleNumber)
ES2.mod.chl.lm##
## Call:
## lm(formula = ES2.mod$ES2_chl.tuk ~ ES2.mod$Treatment + ES2.mod$Dilution +
## ES2.mod$Condition + ES2.mod$isoRep + ES2.mod$techRep + ES2.mod$sampleNumber)
##
## Coefficients:
## (Intercept) ES2.mod$TreatmentDMCC2126
## 7.52662 -2.19660
## ES2.mod$TreatmentDMCC2127 ES2.mod$TreatmentDMCC2165
## -3.39025 -3.45003
## ES2.mod$TreatmentDMCC2966 ES2.mod$Dilution25fold
## -0.21011 -2.34945
## ES2.mod$ConditionStationary ES2.mod$isoRepisolateRep2
## -0.09975 0.73788
## ES2.mod$techRepStem2 ES2.mod$techRepStem3
## -0.70265 -0.27113
## ES2.mod$sampleNumbersample2 ES2.mod$sampleNumbersample3
## -0.03389 -0.09430summary(ES2.mod.chl.lm)##
## Call:
## lm(formula = ES2.mod$ES2_chl.tuk ~ ES2.mod$Treatment + ES2.mod$Dilution +
## ES2.mod$Condition + ES2.mod$isoRep + ES2.mod$techRep + ES2.mod$sampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1829 -1.1889 0.4416 1.2936 4.5838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.52662 0.44329 16.979 < 2e-16 ***
## ES2.mod$TreatmentDMCC2126 -2.19660 0.41262 -5.323 2.11e-07 ***
## ES2.mod$TreatmentDMCC2127 -3.39025 0.41803 -8.110 1.67e-14 ***
## ES2.mod$TreatmentDMCC2165 -3.45003 0.41254 -8.363 3.06e-15 ***
## ES2.mod$TreatmentDMCC2966 -0.21011 0.40229 -0.522 0.60190
## ES2.mod$Dilution25fold -2.34945 0.26117 -8.996 < 2e-16 ***
## ES2.mod$ConditionStationary -0.09975 0.26000 -0.384 0.70152
## ES2.mod$isoRepisolateRep2 0.73788 0.26043 2.833 0.00495 **
## ES2.mod$techRepStem2 -0.70265 0.33340 -2.108 0.03597 *
## ES2.mod$techRepStem3 -0.27113 0.30518 -0.888 0.37510
## ES2.mod$sampleNumbersample2 -0.03389 0.31425 -0.108 0.91420
## ES2.mod$sampleNumbersample3 -0.09430 0.31539 -0.299 0.76518
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.188 on 276 degrees of freedom
## (72 observations deleted due to missingness)
## Multiple R-squared: 0.4559, Adjusted R-squared: 0.4342
## F-statistic: 21.02 on 11 and 276 DF, p-value: < 2.2e-16anova(ES2.mod.chl.lm)## Analysis of Variance Table
##
## Response: ES2.mod$ES2_chl.tuk
## Df Sum Sq Mean Sq F value Pr(>F)
## ES2.mod$Treatment 4 680.08 170.02 35.5165 < 2.2e-16 ***
## ES2.mod$Dilution 1 367.55 367.55 76.7802 < 2.2e-16 ***
## ES2.mod$Condition 1 0.63 0.63 0.1326 0.716072
## ES2.mod$isoRep 1 36.95 36.95 7.7190 0.005839 **
## ES2.mod$techRep 2 21.22 10.61 2.2166 0.110912
## ES2.mod$sampleNumber 2 0.44 0.22 0.0456 0.955457
## Residuals 276 1321.23 4.79
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Tukey's HSD for Variable chl (tukey trans) by Treament
ES2.mod.chl.treatment.HSD.test <- HSD.test(ES2.mod.chl.lm, 'ES2.mod$Treatment', group = T)
ES2.mod.chl.treatment.HSD.test## $statistics
## MSerror Df Mean CV
## 4.787063 276 4.479861 48.83937
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.mod$Treatment 5 3.883285 0.05
##
## $means
## ES2.mod$ES2_chl.tuk std r Min Max Q25 Q50
## control 6.207956 3.276161 57 0 12.443509 3.419937 6.346130
## DMCC2126 4.140619 2.307227 57 0 8.294402 2.483657 3.560255
## DMCC2127 2.929858 2.131941 54 0 6.831014 2.232076 2.783162
## DMCC2165 2.663168 1.976045 57 0 7.124617 2.217514 2.690283
## DMCC2966 6.195529 2.505798 63 0 8.587655 6.006381 6.976629
## Q75
## control 8.191511
## DMCC2126 6.197648
## DMCC2127 4.305207
## DMCC2165 3.099921
## DMCC2966 7.725989
##
## $comparison
## NULL
##
## $groups
## ES2.mod$ES2_chl.tuk groups
## control 6.207956 a
## DMCC2966 6.195529 a
## DMCC2126 4.140619 b
## DMCC2127 2.929858 c
## DMCC2165 2.663168 c
##
## attr(,"class")
## [1] "group"#Tukey's HSD for Variable chl (tukey trans) by Dilution
ES2.mod.chl.dilution.HSD.test <- HSD.test(ES2.mod.chl.lm, 'ES2.mod$Dilution', group = T)
ES2.mod.chl.dilution.HSD.test## $statistics
## MSerror Df Mean CV
## 4.787063 276 4.479861 48.83937
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.mod$Dilution 2 2.784016 0.05
##
## $means
## ES2.mod$ES2_chl.tuk std r Min Max Q25 Q50
## 100fold 5.670079 2.877306 138 0 12.443509 3.837417 6.189452
## 25fold 3.384861 2.482893 150 0 8.587655 2.358352 2.806307
## Q75
## 100fold 7.383524
## 25fold 5.616963
##
## $comparison
## NULL
##
## $groups
## ES2.mod$ES2_chl.tuk groups
## 100fold 5.670079 a
## 25fold 3.384861 b
##
## attr(,"class")
## [1] "group"#Complete ANOVA for ES2.mod by treatment by dilution (tukey trans)
ES2.mod.comp.HSD.group <- HSD.test(ES2.mod.chl.lm, c("ES2.mod$Treatment",
"ES2.mod$Dilution"),
group=TRUE,console=TRUE)##
## Study: ES2.mod.chl.lm ~ c("ES2.mod$Treatment", "ES2.mod$Dilution")
##
## HSD Test for ES2.mod$ES2_chl.tuk
##
## Mean Square Error: 4.787063
##
## ES2.mod$Treatment:ES2.mod$Dilution, means
##
## ES2.mod.ES2_chl.tuk std r Min Max
## control:100fold 8.952842 2.033695 24 5.575585 12.443509
## control:25fold 4.211675 2.459674 33 0.000000 8.147445
## DMCC2126:100fold 5.904452 1.432971 30 2.409370 8.294402
## DMCC2126:25fold 2.180805 1.263683 27 0.000000 4.390190
## DMCC2127:100fold 3.720246 2.309541 27 0.000000 6.831014
## DMCC2127:25fold 2.139470 1.622868 27 0.000000 5.873811
## DMCC2165:100fold 3.677465 2.368645 24 0.000000 7.124617
## DMCC2165:25fold 1.925497 1.211620 33 0.000000 3.041187
## DMCC2966:100fold 6.114039 2.778697 33 0.000000 8.510026
## DMCC2966:25fold 6.285168 2.210961 30 0.000000 8.587655
##
## Alpha: 0.05 ; DF Error: 276
## Critical Value of Studentized Range: 4.511094
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ES2.mod$ES2_chl.tuk groups
## control:100fold 8.952842 a
## DMCC2966:25fold 6.285168 b
## DMCC2966:100fold 6.114039 b
## DMCC2126:100fold 5.904452 bc
## control:25fold 4.211675 cd
## DMCC2127:100fold 3.720246 de
## DMCC2165:100fold 3.677465 de
## DMCC2126:25fold 2.180805 e
## DMCC2127:25fold 2.139470 e
## DMCC2165:25fold 1.925497 eES2.mod.comp.HSD.group## $statistics
## MSerror Df Mean CV
## 4.787063 276 4.479861 48.83937
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.mod$Treatment:ES2.mod$Dilution 10 4.511094 0.05
##
## $means
## ES2.mod$ES2_chl.tuk std r Min Max Q25
## control:100fold 8.952842 2.033695 24 5.575585 12.443509 7.860042
## control:25fold 4.211675 2.459674 33 0.000000 8.147445 2.807281
## DMCC2126:100fold 5.904452 1.432971 30 2.409370 8.294402 5.211560
## DMCC2126:25fold 2.180805 1.263683 27 0.000000 4.390190 2.359361
## DMCC2127:100fold 3.720246 2.309541 27 0.000000 6.831014 2.547399
## DMCC2127:25fold 2.139470 1.622868 27 0.000000 5.873811 0.000000
## DMCC2165:100fold 3.677465 2.368645 24 0.000000 7.124617 2.700544
## DMCC2165:25fold 1.925497 1.211620 33 0.000000 3.041187 0.000000
## DMCC2966:100fold 6.114039 2.778697 33 0.000000 8.510026 5.991199
## DMCC2966:25fold 6.285168 2.210961 30 0.000000 8.587655 6.035946
## Q50 Q75
## control:100fold 8.279323 10.486003
## control:25fold 4.628247 6.200641
## DMCC2126:100fold 6.189452 6.705312
## DMCC2126:25fold 2.483657 2.863395
## DMCC2127:100fold 3.818594 5.417472
## DMCC2127:25fold 2.531540 2.807481
## DMCC2165:100fold 3.362478 5.745663
## DMCC2165:25fold 2.561469 2.745123
## DMCC2966:100fold 6.963949 7.832392
## DMCC2966:25fold 7.032779 7.590879
##
## $comparison
## NULL
##
## $groups
## ES2.mod$ES2_chl.tuk groups
## control:100fold 8.952842 a
## DMCC2966:25fold 6.285168 b
## DMCC2966:100fold 6.114039 b
## DMCC2126:100fold 5.904452 bc
## control:25fold 4.211675 cd
## DMCC2127:100fold 3.720246 de
## DMCC2165:100fold 3.677465 de
## DMCC2126:25fold 2.180805 e
## DMCC2127:25fold 2.139470 e
## DMCC2165:25fold 1.925497 e
##
## attr(,"class")
## [1] "group"Run analyses for ES5
This test was run for 7 DOE and photos were taken of the last day of exposure.
###############ES5 analysis################################
ES5.chl.lm <- lm (ES5$chl ~ ES5$Treatment +
ES5$Dilution +
ES5$Condition +
ES5$isoRep +
ES5$techRep +
ES5$sampleNumber)
ES5.chl.lm##
## Call:
## lm(formula = ES5$chl ~ ES5$Treatment + ES5$Dilution + ES5$Condition +
## ES5$isoRep + ES5$techRep + ES5$sampleNumber)
##
## Coefficients:
## (Intercept) ES5$TreatmentDMCC2126 ES5$TreatmentDMCC2127
## 192.365 -61.618 -70.990
## ES5$TreatmentDMCC2165 ES5$Dilution25fold ES5$ConditionStationary
## -67.429 -46.539 42.178
## ES5$isoRepisolateRep2 ES5$isoRepisolateRep3 ES5$techRepstemRep2
## -9.981 -22.792 -14.269
## ES5$techRepstemRep3 ES5$sampleNumbersample2 ES5$sampleNumbersample3
## 19.985 11.399 25.312summary(ES5.chl.lm)##
## Call:
## lm(formula = ES5$chl ~ ES5$Treatment + ES5$Dilution + ES5$Condition +
## ES5$isoRep + ES5$techRep + ES5$sampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -182.445 -40.817 -5.474 42.676 187.396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 192.365 10.836 17.753 < 2e-16 ***
## ES5$TreatmentDMCC2126 -61.618 8.801 -7.001 1.05e-11 ***
## ES5$TreatmentDMCC2127 -70.990 8.734 -8.128 5.27e-15 ***
## ES5$TreatmentDMCC2165 -67.429 8.798 -7.664 1.33e-13 ***
## ES5$Dilution25fold -46.539 6.177 -7.534 3.19e-13 ***
## ES5$ConditionStationary 42.178 6.177 6.828 3.13e-11 ***
## ES5$isoRepisolateRep2 -9.981 7.580 -1.317 0.188662
## ES5$isoRepisolateRep3 -22.792 7.534 -3.025 0.002642 **
## ES5$techRepstemRep2 -14.269 7.620 -1.873 0.061849 .
## ES5$techRepstemRep3 19.985 7.536 2.652 0.008315 **
## ES5$sampleNumbersample2 11.399 7.557 1.509 0.132197
## ES5$sampleNumbersample3 25.312 7.557 3.350 0.000884 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 63.22 on 408 degrees of freedom
## (12 observations deleted due to missingness)
## Multiple R-squared: 0.3665, Adjusted R-squared: 0.3494
## F-statistic: 21.46 on 11 and 408 DF, p-value: < 2.2e-16anova(ES5.chl.lm)## Analysis of Variance Table
##
## Response: ES5$chl
## Df Sum Sq Mean Sq F value Pr(>F)
## ES5$Treatment 3 351053 117018 29.2750 < 2.2e-16 ***
## ES5$Dilution 1 239796 239796 59.9912 7.615e-14 ***
## ES5$Condition 1 186231 186231 46.5904 3.179e-11 ***
## ES5$isoRep 2 37850 18925 4.7345 0.009275 **
## ES5$techRep 2 83616 41808 10.4593 3.717e-05 ***
## ES5$sampleNumber 2 44997 22498 5.6285 0.003879 **
## Residuals 408 1630853 3997
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Tukey's HSD for Variable chl by Treament
ES5.chl.treatment.HSD.test <- HSD.test(ES5.chl.lm, 'ES5$Treatment', group = T)
ES5.chl.treatment.HSD.test## $statistics
## MSerror Df Mean CV
## 3997.188 408 143.1371 44.16975
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES5$Treatment 4 3.648176 0.05
##
## $means
## ES5$chl std r Min Max Q25 Q50 Q75
## control 193.8353 69.20948 102 26.0 372.6 147.750 202.9 240.55
## DMCC2126 131.8714 73.80466 105 30.3 277.2 63.700 110.8 189.60
## DMCC2127 122.4120 75.41655 108 0.0 339.2 64.875 100.3 157.95
## DMCC2165 126.4705 73.67261 105 0.0 289.2 68.300 100.0 189.20
##
## $comparison
## NULL
##
## $groups
## ES5$chl groups
## control 193.8353 a
## DMCC2126 131.8714 b
## DMCC2165 126.4705 b
## DMCC2127 122.4120 b
##
## attr(,"class")
## [1] "group"#Tukey's HSD for Variable chl by Dilution
ES5.chl.dilution.HSD.test <- HSD.test(ES5.chl.lm, 'ES5$Dilution', group = T)
ES5.chl.dilution.HSD.test## $statistics
## MSerror Df Mean CV MSD
## 3997.188 408 143.1371 44.16975 12.12889
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES5$Dilution 2 2.780054 0.05
##
## $means
## ES5$chl std r Min Max Q25 Q50 Q75
## 100fold 166.9881 77.60533 210 0 372.6 99.475 178.20 232.825
## 25fold 119.2862 71.77681 210 0 303.7 61.000 94.45 174.500
##
## $comparison
## NULL
##
## $groups
## ES5$chl groups
## 100fold 166.9881 a
## 25fold 119.2862 b
##
## attr(,"class")
## [1] "group"#Tukey's HSD for Variable chl by Condition
ES5.chl.cond.HSD.test <- HSD.test(ES5.chl.lm, 'ES5$Condition', group = T)
ES5.chl.cond.HSD.test## $statistics
## MSerror Df Mean CV MSD
## 3997.188 408 143.1371 44.16975 12.12889
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES5$Condition 2 2.780054 0.05
##
## $means
## ES5$chl std r Min Max Q25 Q50 Q75
## Shaking 121.3619 70.21004 210 0 363.9 63.900 99.15 174.075
## Stationary 164.9124 80.22074 210 0 372.6 91.075 179.40 234.325
##
## $comparison
## NULL
##
## $groups
## ES5$chl groups
## Stationary 164.9124 a
## Shaking 121.3619 b
##
## attr(,"class")
## [1] "group"#Complete ANOVA for ES5 by treatment by condition, by dilution
ES5.comp.HSD.group <- HSD.test(ES5.chl.lm, c("ES5$Treatment", "ES5$Condition",
"ES5$Dilution"), group=TRUE,console=TRUE)##
## Study: ES5.chl.lm ~ c("ES5$Treatment", "ES5$Condition", "ES5$Dilution")
##
## HSD Test for ES5$chl
##
## Mean Square Error: 3997.188
##
## ES5$Treatment:ES5$Condition:ES5$Dilution, means
##
## ES5.chl std r Min Max
## control:Shaking:100fold 200.02083 68.81458 24 104.0 363.9
## control:Shaking:25fold 158.22593 62.18883 27 26.0 249.0
## control:Stationary:100fold 238.50000 37.84527 27 185.5 372.6
## control:Stationary:25fold 177.46250 78.47053 24 37.7 303.7
## DMCC2126:Shaking:100fold 161.77500 70.00547 24 48.0 270.4
## DMCC2126:Shaking:25fold 75.53333 30.56325 27 30.3 140.8
## DMCC2126:Stationary:100fold 174.24815 63.63720 27 51.7 264.4
## DMCC2126:Stationary:25fold 119.25185 79.48387 27 36.0 277.2
## DMCC2127:Shaking:100fold 93.23333 39.13111 27 37.1 190.5
## DMCC2127:Shaking:25fold 61.84444 32.99067 27 0.0 119.5
## DMCC2127:Stationary:100fold 192.10370 77.79170 27 75.0 339.2
## DMCC2127:Stationary:25fold 142.46667 67.68053 27 53.1 296.0
## DMCC2165:Shaking:100fold 143.79630 71.66806 27 36.8 273.2
## DMCC2165:Shaking:25fold 89.69630 40.84195 27 40.3 174.5
## DMCC2165:Stationary:100fold 135.31852 85.93666 27 0.0 279.9
## DMCC2165:Stationary:25fold 138.39583 79.51052 24 48.0 289.2
##
## Alpha: 0.05 ; DF Error: 408
## Critical Value of Studentized Range: 4.87582
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ES5$chl groups
## control:Stationary:100fold 238.50000 a
## control:Shaking:100fold 200.02083 ab
## DMCC2127:Stationary:100fold 192.10370 abc
## control:Stationary:25fold 177.46250 abcd
## DMCC2126:Stationary:100fold 174.24815 bcd
## DMCC2126:Shaking:100fold 161.77500 bcd
## control:Shaking:25fold 158.22593 bcd
## DMCC2165:Shaking:100fold 143.79630 bcde
## DMCC2127:Stationary:25fold 142.46667 bcde
## DMCC2165:Stationary:25fold 138.39583 bcde
## DMCC2165:Stationary:100fold 135.31852 cde
## DMCC2126:Stationary:25fold 119.25185 def
## DMCC2127:Shaking:100fold 93.23333 ef
## DMCC2165:Shaking:25fold 89.69630 ef
## DMCC2126:Shaking:25fold 75.53333 f
## DMCC2127:Shaking:25fold 61.84444 fES5.comp.HSD.group## $statistics
## MSerror Df Mean CV
## 3997.188 408 143.1371 44.16975
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES5$Treatment:ES5$Condition:ES5$Dilution 16 4.87582 0.05
##
## $means
## ES5$chl std r Min Max Q25 Q50
## control:Shaking:100fold 200.02083 68.81458 24 104.0 363.9 146.800 186.90
## control:Shaking:25fold 158.22593 62.18883 27 26.0 249.0 126.400 174.80
## control:Stationary:100fold 238.50000 37.84527 27 185.5 372.6 215.850 235.10
## control:Stationary:25fold 177.46250 78.47053 24 37.7 303.7 124.350 193.05
## DMCC2126:Shaking:100fold 161.77500 70.00547 24 48.0 270.4 87.125 173.00
## DMCC2126:Shaking:25fold 75.53333 30.56325 27 30.3 140.8 50.050 66.50
## DMCC2126:Stationary:100fold 174.24815 63.63720 27 51.7 264.4 132.600 180.00
## DMCC2126:Stationary:25fold 119.25185 79.48387 27 36.0 277.2 56.400 85.00
## DMCC2127:Shaking:100fold 93.23333 39.13111 27 37.1 190.5 69.800 85.80
## DMCC2127:Shaking:25fold 61.84444 32.99067 27 0.0 119.5 45.400 58.90
## DMCC2127:Stationary:100fold 192.10370 77.79170 27 75.0 339.2 109.700 204.20
## DMCC2127:Stationary:25fold 142.46667 67.68053 27 53.1 296.0 78.050 131.20
## DMCC2165:Shaking:100fold 143.79630 71.66806 27 36.8 273.2 78.650 113.60
## DMCC2165:Shaking:25fold 89.69630 40.84195 27 40.3 174.5 60.100 77.90
## DMCC2165:Stationary:100fold 135.31852 85.93666 27 0.0 279.9 61.950 158.00
## DMCC2165:Stationary:25fold 138.39583 79.51052 24 48.0 289.2 73.150 114.70
## Q75
## control:Shaking:100fold 245.325
## control:Shaking:25fold 205.700
## control:Stationary:100fold 253.200
## control:Stationary:25fold 238.750
## DMCC2126:Shaking:100fold 207.875
## DMCC2126:Shaking:25fold 94.400
## DMCC2126:Stationary:100fold 230.800
## DMCC2126:Stationary:25fold 173.250
## DMCC2127:Shaking:100fold 116.550
## DMCC2127:Shaking:25fold 88.250
## DMCC2127:Stationary:100fold 249.300
## DMCC2127:Stationary:25fold 186.700
## DMCC2165:Shaking:100fold 201.750
## DMCC2165:Shaking:25fold 94.600
## DMCC2165:Stationary:100fold 205.300
## DMCC2165:Stationary:25fold 191.025
##
## $comparison
## NULL
##
## $groups
## ES5$chl groups
## control:Stationary:100fold 238.50000 a
## control:Shaking:100fold 200.02083 ab
## DMCC2127:Stationary:100fold 192.10370 abc
## control:Stationary:25fold 177.46250 abcd
## DMCC2126:Stationary:100fold 174.24815 bcd
## DMCC2126:Shaking:100fold 161.77500 bcd
## control:Shaking:25fold 158.22593 bcd
## DMCC2165:Shaking:100fold 143.79630 bcde
## DMCC2127:Stationary:25fold 142.46667 bcde
## DMCC2165:Stationary:25fold 138.39583 bcde
## DMCC2165:Stationary:100fold 135.31852 cde
## DMCC2126:Stationary:25fold 119.25185 def
## DMCC2127:Shaking:100fold 93.23333 ef
## DMCC2165:Shaking:25fold 89.69630 ef
## DMCC2126:Shaking:25fold 75.53333 f
## DMCC2127:Shaking:25fold 61.84444 f
##
## attr(,"class")
## [1] "group"Same analyses for ES5, using normalized data
###############ES5 analysis (normalized data) ################################
ES5.mod.chl.lm <- lm (ES5.mod$ES5_chl.tuk ~ ES5.mod$Treatment +
ES5.mod$Dilution +
ES5.mod$Condition +
ES5.mod$isoRep +
ES5.mod$techRep +
ES5.mod$sampleNumber)
ES5.mod.chl.lm##
## Call:
## lm(formula = ES5.mod$ES5_chl.tuk ~ ES5.mod$Treatment + ES5.mod$Dilution +
## ES5.mod$Condition + ES5.mod$isoRep + ES5.mod$techRep + ES5.mod$sampleNumber)
##
## Coefficients:
## (Intercept) ES5.mod$TreatmentDMCC2126
## 30.278 -7.067
## ES5.mod$TreatmentDMCC2127 ES5.mod$TreatmentDMCC2165
## -8.357 -7.928
## ES5.mod$Dilution25fold ES5.mod$ConditionStationary
## -5.443 4.789
## ES5.mod$isoRepisolateRep2 ES5.mod$isoRepisolateRep3
## -1.403 -2.930
## ES5.mod$techRepstemRep2 ES5.mod$techRepstemRep3
## -1.392 2.517
## ES5.mod$sampleNumbersample2 ES5.mod$sampleNumbersample3
## 1.050 2.548summary(ES5.mod.chl.lm)##
## Call:
## lm(formula = ES5.mod$ES5_chl.tuk ~ ES5.mod$Treatment + ES5.mod$Dilution +
## ES5.mod$Condition + ES5.mod$isoRep + ES5.mod$techRep + ES5.mod$sampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.2842 -4.6883 -0.0798 5.3904 19.2000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 30.2777 1.3012 23.270 < 2e-16 ***
## ES5.mod$TreatmentDMCC2126 -7.0672 1.0568 -6.687 7.50e-11 ***
## ES5.mod$TreatmentDMCC2127 -8.3567 1.0488 -7.968 1.63e-14 ***
## ES5.mod$TreatmentDMCC2165 -7.9283 1.0565 -7.505 3.90e-13 ***
## ES5.mod$Dilution25fold -5.4428 0.7417 -7.338 1.18e-12 ***
## ES5.mod$ConditionStationary 4.7890 0.7417 6.457 3.05e-10 ***
## ES5.mod$isoRepisolateRep2 -1.4026 0.9102 -1.541 0.12411
## ES5.mod$isoRepisolateRep3 -2.9300 0.9047 -3.239 0.00130 **
## ES5.mod$techRepstemRep2 -1.3920 0.9150 -1.521 0.12896
## ES5.mod$techRepstemRep3 2.5171 0.9049 2.782 0.00566 **
## ES5.mod$sampleNumbersample2 1.0500 0.9074 1.157 0.24789
## ES5.mod$sampleNumbersample3 2.5484 0.9074 2.808 0.00522 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.592 on 408 degrees of freedom
## (12 observations deleted due to missingness)
## Multiple R-squared: 0.3506, Adjusted R-squared: 0.3331
## F-statistic: 20.02 on 11 and 408 DF, p-value: < 2.2e-16anova(ES5.mod.chl.lm)## Analysis of Variance Table
##
## Response: ES5.mod$ES5_chl.tuk
## Df Sum Sq Mean Sq F value Pr(>F)
## ES5.mod$Treatment 3 4830.6 1610.2 27.9375 < 2.2e-16 ***
## ES5.mod$Dilution 1 3271.4 3271.4 56.7598 3.204e-13 ***
## ES5.mod$Condition 1 2403.9 2403.9 41.7082 3.018e-10 ***
## ES5.mod$isoRep 2 618.1 309.1 5.3623 0.005027 **
## ES5.mod$techRep 2 1110.4 555.2 9.6327 8.172e-05 ***
## ES5.mod$sampleNumber 2 459.3 229.6 3.9845 0.019330 *
## Residuals 408 23515.2 57.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Tukey's HSD for Variable chl (tukey trans) by Treament
ES5.mod.chl.treatment.HSD.test <- HSD.test(ES5.mod.chl.lm, 'ES5.mod$Treatment',
group = T)
ES5.mod.chl.treatment.HSD.test## $statistics
## MSerror Df Mean CV
## 57.63529 408 24.21363 31.35338
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES5.mod$Treatment 4 3.648176 0.05
##
## $means
## ES5.mod$ES5_chl.tuk std r Min Max Q25 Q50
## control 30.14562 7.609997 102 8.312519 46.91458 25.71521 31.60326
## DMCC2126 23.01374 8.709822 105 9.182009 38.70932 14.88301 21.32803
## DMCC2127 21.72436 9.135639 108 0.000000 44.13634 15.06086 19.99151
## DMCC2165 22.21139 9.151154 105 0.000000 39.79045 15.57304 19.95262
## Q75
## control 35.30039
## DMCC2126 30.24091
## DMCC2127 26.85501
## DMCC2165 30.19943
##
## $comparison
## NULL
##
## $groups
## ES5.mod$ES5_chl.tuk groups
## control 30.14562 a
## DMCC2126 23.01374 b
## DMCC2165 22.21139 b
## DMCC2127 21.72436 b
##
## attr(,"class")
## [1] "group"#Tukey's HSD for Variable chl (tukey trans) by Dilution
ES5.mod.chl.dilution.HSD.test <- HSD.test(ES5.mod.chl.lm, 'ES5.mod$Dilution',
group = T)
ES5.mod.chl.dilution.HSD.test## $statistics
## MSerror Df Mean CV MSD
## 57.63529 408 24.21363 31.35338 1.456424
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES5.mod$Dilution 2 2.780054 0.05
##
## $means
## ES5.mod$ES5_chl.tuk std r Min Max Q25 Q50
## 100fold 26.99820 9.029696 210 0 46.91458 19.88445 29.04621
## 25fold 21.42906 8.725273 210 0 41.07609 14.46985 19.22561
## Q75
## 100fold 34.55964
## 25fold 28.65280
##
## $comparison
## NULL
##
## $groups
## ES5.mod$ES5_chl.tuk groups
## 100fold 26.99820 a
## 25fold 21.42906 b
##
## attr(,"class")
## [1] "group"#Tukey's HSD for Variable chl (tukey trans) by Condition
ES5.mod.chl.cond.HSD.test <- HSD.test(ES5.mod.chl.lm, 'ES5.mod$Condition', group = T)
ES5.mod.chl.cond.HSD.test## $statistics
## MSerror Df Mean CV MSD
## 57.63529 408 24.21363 31.35338 1.456424
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES5.mod$Condition 2 2.780054 0.05
##
## $means
## ES5.mod$ES5_chl.tuk std r Min Max Q25 Q50
## Shaking 21.73611 8.509070 210 0 46.19961 14.91331 19.84222
## Stationary 26.69114 9.407897 210 0 46.91458 18.77627 29.17320
## Q75
## Shaking 28.60736
## Stationary 34.70420
##
## $comparison
## NULL
##
## $groups
## ES5.mod$ES5_chl.tuk groups
## Stationary 26.69114 a
## Shaking 21.73611 b
##
## attr(,"class")
## [1] "group"#Complete ANOVA for ES5.mod by treatment by condition, by dilution (tukey trans)
ES5.mod.comp.HSD.group <- HSD.test(ES5.mod.chl.lm, c("ES5.mod$Treatment",
"ES5.mod$Condition",
"ES5.mod$Dilution"),
group=TRUE,console=TRUE)##
## Study: ES5.mod.chl.lm ~ c("ES5.mod$Treatment", "ES5.mod$Condition", "ES5.mod$Dilution")
##
## HSD Test for ES5.mod$ES5_chl.tuk
##
## Mean Square Error: 57.63529
##
## ES5.mod$Treatment:ES5.mod$Condition:ES5.mod$Dilution, means
##
## ES5.mod.ES5_chl.tuk std r Min Max
## control:Shaking:100fold 30.92160 6.877550 24 20.467824 46.19961
## control:Shaking:25fold 26.28822 7.584437 27 8.312519 36.10186
## control:Stationary:100fold 35.01510 3.511991 27 29.814226 46.91458
## control:Stationary:25fold 28.23105 8.847762 24 10.583319 41.07609
## DMCC2126:Shaking:100fold 26.63321 8.061394 24 12.382456 38.08942
## DMCC2126:Shaking:25fold 16.33068 4.334703 27 9.182009 24.92251
## DMCC2126:Stationary:100fold 28.13045 7.218510 27 12.994778 37.53790
## DMCC2126:Stationary:25fold 21.36280 9.304178 27 10.270619 38.70932
## DMCC2127:Shaking:100fold 18.70574 5.119409 27 10.473529 30.33414
## DMCC2127:Shaking:25fold 13.80400 6.101857 27 0.000000 22.40212
## DMCC2127:Stationary:100fold 29.90084 8.268740 27 16.549688 44.13634
## DMCC2127:Stationary:25fold 24.48688 7.726440 27 13.222435 40.39612
## DMCC2165:Shaking:100fold 24.56023 8.205647 27 10.418401 38.34533
## DMCC2165:Shaking:25fold 18.20998 5.258465 27 11.052188 28.65280
## DMCC2165:Stationary:100fold 22.51380 11.836979 27 0.000000 38.95398
## DMCC2165:Stationary:25fold 23.73032 9.225861 24 12.382456 39.79045
##
## Alpha: 0.05 ; DF Error: 408
## Critical Value of Studentized Range: 4.87582
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ES5.mod$ES5_chl.tuk groups
## control:Stationary:100fold 35.01510 a
## control:Shaking:100fold 30.92160 ab
## DMCC2127:Stationary:100fold 29.90084 ab
## control:Stationary:25fold 28.23105 abc
## DMCC2126:Stationary:100fold 28.13045 abc
## DMCC2126:Shaking:100fold 26.63321 bc
## control:Shaking:25fold 26.28822 bc
## DMCC2165:Shaking:100fold 24.56023 bcd
## DMCC2127:Stationary:25fold 24.48688 bcd
## DMCC2165:Stationary:25fold 23.73032 bcd
## DMCC2165:Stationary:100fold 22.51380 cde
## DMCC2126:Stationary:25fold 21.36280 cde
## DMCC2127:Shaking:100fold 18.70574 def
## DMCC2165:Shaking:25fold 18.20998 def
## DMCC2126:Shaking:25fold 16.33068 ef
## DMCC2127:Shaking:25fold 13.80400 fES5.mod.comp.HSD.group## $statistics
## MSerror Df Mean CV
## 57.63529 408 24.21363 31.35338
##
## $parameters
## test name.t ntr
## Tukey ES5.mod$Treatment:ES5.mod$Condition:ES5.mod$Dilution 16
## StudentizedRange alpha
## 4.87582 0.05
##
## $means
## ES5.mod$ES5_chl.tuk std r Min Max
## control:Shaking:100fold 30.92160 6.877550 24 20.467824 46.19961
## control:Shaking:25fold 26.28822 7.584437 27 8.312519 36.10186
## control:Stationary:100fold 35.01510 3.511991 27 29.814226 46.91458
## control:Stationary:25fold 28.23105 8.847762 24 10.583319 41.07609
## DMCC2126:Shaking:100fold 26.63321 8.061394 24 12.382456 38.08942
## DMCC2126:Shaking:25fold 16.33068 4.334703 27 9.182009 24.92251
## DMCC2126:Stationary:100fold 28.13045 7.218510 27 12.994778 37.53790
## DMCC2126:Stationary:25fold 21.36280 9.304178 27 10.270619 38.70932
## DMCC2127:Shaking:100fold 18.70574 5.119409 27 10.473529 30.33414
## DMCC2127:Shaking:25fold 13.80400 6.101857 27 0.000000 22.40212
## DMCC2127:Stationary:100fold 29.90084 8.268740 27 16.549688 44.13634
## DMCC2127:Stationary:25fold 24.48688 7.726440 27 13.222435 40.39612
## DMCC2165:Shaking:100fold 24.56023 8.205647 27 10.418401 38.34533
## DMCC2165:Shaking:25fold 18.20998 5.258465 27 11.052188 28.65280
## DMCC2165:Stationary:100fold 22.51380 11.836979 27 0.000000 38.95398
## DMCC2165:Stationary:25fold 23.73032 9.225861 24 12.382456 39.79045
## Q25 Q50 Q75
## control:Shaking:100fold 25.60774 29.95578 35.75395
## control:Shaking:25fold 23.23414 28.68481 31.88435
## control:Stationary:100fold 32.89985 34.77877 36.49626
## control:Stationary:25fold 22.97996 30.57655 35.12646
## DMCC2126:Shaking:100fold 18.24222 28.49017 32.10254
## DMCC2126:Shaking:25fold 12.72170 15.30503 19.21892
## DMCC2126:Stationary:100fold 23.96670 29.23662 34.36364
## DMCC2126:Stationary:25fold 13.75020 17.95239 28.49754
## DMCC2127:Shaking:100fold 15.79238 18.06204 22.03937
## DMCC2127:Shaking:25fold 11.94225 14.14409 18.38373
## DMCC2127:Stationary:100fold 21.18901 31.73483 36.13001
## DMCC2127:Stationary:25fold 16.98374 23.80439 29.93923
## DMCC2165:Shaking:100fold 17.06819 21.67684 31.48681
## DMCC2165:Shaking:25fold 14.32943 16.96287 19.24541
## DMCC2165:Stationary:100fold 14.61527 26.86130 31.84574
## DMCC2165:Stationary:25fold 16.28243 21.81134 30.38458
##
## $comparison
## NULL
##
## $groups
## ES5.mod$ES5_chl.tuk groups
## control:Stationary:100fold 35.01510 a
## control:Shaking:100fold 30.92160 ab
## DMCC2127:Stationary:100fold 29.90084 ab
## control:Stationary:25fold 28.23105 abc
## DMCC2126:Stationary:100fold 28.13045 abc
## DMCC2126:Shaking:100fold 26.63321 bc
## control:Shaking:25fold 26.28822 bc
## DMCC2165:Shaking:100fold 24.56023 bcd
## DMCC2127:Stationary:25fold 24.48688 bcd
## DMCC2165:Stationary:25fold 23.73032 bcd
## DMCC2165:Stationary:100fold 22.51380 cde
## DMCC2126:Stationary:25fold 21.36280 cde
## DMCC2127:Shaking:100fold 18.70574 def
## DMCC2165:Shaking:25fold 18.20998 def
## DMCC2126:Shaking:25fold 16.33068 ef
## DMCC2127:Shaking:25fold 13.80400 f
##
## attr(,"class")
## [1] "group"Run analyses for ES13B
Testing variation among potentially resistant cultivars compared to known susceptible cultivars treated with CFCFs from X. necrophora (isolate DMCC 2165) to determine if resistance to direct application of SMs exist.
#Statistical analysis
#####ES13B###
ES13B.chl.lm <- lm (ES13B$chl ~ ES13B$Treatment +
ES13B$HostVariety +
ES13B$isoRepNumber +
ES13B$techRepNumber +
ES13B$SampleNumber)
ES13B.chl.lm##
## Call:
## lm(formula = ES13B$chl ~ ES13B$Treatment + ES13B$HostVariety +
## ES13B$isoRepNumber + ES13B$techRepNumber + ES13B$SampleNumber)
##
## Coefficients:
## (Intercept) ES13B$TreatmentDMCC2165
## 187.9400 -105.4678
## ES13B$HostVarietyDG47E80 ES13B$HostVarietyDG47X95
## 27.8736 26.3892
## ES13B$HostVarietyOsage ES13B$HostVarietyP5414LLS
## 16.1981 -3.8273
## ES13B$isoRepNumberisoRep2 ES13B$isoRepNumberisoRep3
## -7.3121 1.4292
## ES13B$techRepNumbertechRep2 ES13B$techRepNumbertechRep3
## 29.6658 8.0253
## ES13B$SampleNumbersample2 ES13B$SampleNumbersample3
## 0.7302 1.9473summary(ES13B.chl.lm)##
## Call:
## lm(formula = ES13B$chl ~ ES13B$Treatment + ES13B$HostVariety +
## ES13B$isoRepNumber + ES13B$techRepNumber + ES13B$SampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -219.035 -47.751 -4.823 42.506 237.651
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 187.9400 17.1352 10.968 <2e-16 ***
## ES13B$TreatmentDMCC2165 -105.4678 9.9569 -10.592 <2e-16 ***
## ES13B$HostVarietyDG47E80 27.8736 15.5401 1.794 0.0741 .
## ES13B$HostVarietyDG47X95 26.3892 16.0431 1.645 0.1012
## ES13B$HostVarietyOsage 16.1981 15.5401 1.042 0.2983
## ES13B$HostVarietyP5414LLS -3.8273 15.5401 -0.246 0.8057
## ES13B$isoRepNumberisoRep2 -7.3121 12.2504 -0.597 0.5511
## ES13B$isoRepNumberisoRep3 1.4292 12.1499 0.118 0.9065
## ES13B$techRepNumbertechRep2 29.6658 12.1499 2.442 0.0153 *
## ES13B$techRepNumbertechRep3 8.0253 12.1499 0.661 0.5095
## ES13B$SampleNumbersample2 0.7302 12.1733 0.060 0.9522
## ES13B$SampleNumbersample3 1.9473 12.1733 0.160 0.8730
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 80.75 on 252 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.337, Adjusted R-squared: 0.308
## F-statistic: 11.64 on 11 and 252 DF, p-value: < 2.2e-16anova(ES13B.chl.lm)## Analysis of Variance Table
##
## Response: ES13B$chl
## Df Sum Sq Mean Sq F value Pr(>F)
## ES13B$Treatment 1 745236 745236 114.2939 < 2e-16 ***
## ES13B$HostVariety 4 44757 11189 1.7160 0.14689
## ES13B$isoRepNumber 2 3558 1779 0.2728 0.76144
## ES13B$techRepNumber 2 41380 20690 3.1731 0.04355 *
## ES13B$SampleNumber 2 170 85 0.0131 0.98702
## Residuals 252 1643127 6520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Tukey's HSD for Variable chl by Treatment
ES13B.chl.treatment.HSD.test <- HSD.test(ES13B.chl.lm, 'ES13B$Treatment', group = T)
ES13B.chl.treatment.HSD.test## $statistics
## MSerror Df Mean CV
## 6520.345 252 160.8255 50.20887
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES13B$Treatment 2 2.785184 0.05
##
## $means
## ES13B$chl std r Min Max Q25 Q50 Q75
## Control 212.7620 79.79142 135 0 402.241 166.577 220.922 257.822
## DMCC2165 106.4733 82.90892 129 0 350.226 51.563 71.243 161.827
##
## $comparison
## NULL
##
## $groups
## ES13B$chl groups
## Control 212.7620 a
## DMCC2165 106.4733 b
##
## attr(,"class")
## [1] "group"#Tukey's HSD for Variable chl by Soybean Cultivar
ES13B.chl.host_variety.HSD.test <- HSD.test(ES13B.chl.lm, 'ES13B$HostVariety', group = T)
ES13B.chl.host_variety.HSD.test## $statistics
## MSerror Df Mean CV
## 6520.345 252 160.8255 50.20887
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES13B$HostVariety 5 3.885737 0.05
##
## $means
## ES13B$chl std r Min Max Q25 Q50 Q75
## AG4632 146.7014 89.25074 54 0 364.618 68.0405 134.5050 212.5315
## DG47E80 174.5750 94.89959 54 0 372.762 94.9610 203.4325 233.2080
## DG47X95 179.1090 97.67480 48 0 359.307 86.1180 192.7220 255.9690
## Osage 162.8995 111.37700 54 0 402.241 66.9080 155.3500 248.0178
## P5414LLS 142.8741 88.83067 54 0 318.243 59.8260 153.8800 221.6343
##
## $comparison
## NULL
##
## $groups
## ES13B$chl groups
## DG47X95 179.1090 a
## DG47E80 174.5750 a
## Osage 162.8995 a
## AG4632 146.7014 a
## P5414LLS 142.8741 a
##
## attr(,"class")
## [1] "group"#Complete ANOVA for ES13B
ES13B.comp.HSD.group <- HSD.test(ES13B.chl.lm, c("ES13B$Treatment", "ES13B$HostVariety"),
group=TRUE,console=TRUE)##
## Study: ES13B.chl.lm ~ c("ES13B$Treatment", "ES13B$HostVariety")
##
## HSD Test for ES13B$chl
##
## Mean Square Error: 6520.345
##
## ES13B$Treatment:ES13B$HostVariety, means
##
## ES13B.chl std r Min Max
## Control:AG4632 190.99715 86.60398 27 0.000 364.618
## Control:DG47E80 228.60578 74.03698 27 99.638 372.762
## Control:DG47X95 217.34011 75.28029 27 62.560 359.307
## Control:Osage 236.66259 98.93830 27 0.000 402.241
## Control:P5414LLS 190.20437 49.79161 27 96.055 269.571
## DMCC2165:AG4632 102.40559 68.28138 27 0.000 279.119
## DMCC2165:DG47E80 120.54422 82.54428 27 0.000 268.043
## DMCC2165:DG47X95 129.95467 102.67650 21 0.000 350.226
## DMCC2165:Osage 89.13633 64.78778 27 0.000 305.544
## DMCC2165:P5414LLS 95.54374 94.62256 27 0.000 318.243
##
## Alpha: 0.05 ; DF Error: 252
## Critical Value of Studentized Range: 4.514628
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ES13B$chl groups
## Control:Osage 236.66259 a
## Control:DG47E80 228.60578 a
## Control:DG47X95 217.34011 a
## Control:AG4632 190.99715 ab
## Control:P5414LLS 190.20437 abc
## DMCC2165:DG47X95 129.95467 bcd
## DMCC2165:DG47E80 120.54422 cd
## DMCC2165:AG4632 102.40559 d
## DMCC2165:P5414LLS 95.54374 d
## DMCC2165:Osage 89.13633 dES13B.comp.HSD.group## $statistics
## MSerror Df Mean CV
## 6520.345 252 160.8255 50.20887
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES13B$Treatment:ES13B$HostVariety 10 4.514628 0.05
##
## $means
## ES13B$chl std r Min Max Q25 Q50
## Control:AG4632 190.99715 86.60398 27 0.000 364.618 144.0055 209.592
## Control:DG47E80 228.60578 74.03698 27 99.638 372.762 206.6285 227.869
## Control:DG47X95 217.34011 75.28029 27 62.560 359.307 180.9375 220.770
## Control:Osage 236.66259 98.93830 27 0.000 402.241 220.1595 246.824
## Control:P5414LLS 190.20437 49.79161 27 96.055 269.571 163.7070 193.690
## DMCC2165:AG4632 102.40559 68.28138 27 0.000 279.119 55.0810 79.594
## DMCC2165:DG47E80 120.54422 82.54428 27 0.000 268.043 49.0770 93.402
## DMCC2165:DG47X95 129.95467 102.67650 21 0.000 350.226 47.3850 81.525
## DMCC2165:Osage 89.13633 64.78778 27 0.000 305.544 63.5255 67.114
## DMCC2165:P5414LLS 95.54374 94.62256 27 0.000 318.243 35.1075 57.848
## Q75
## Control:AG4632 234.2065
## Control:DG47E80 277.7645
## Control:DG47X95 275.6780
## Control:Osage 271.3380
## Control:P5414LLS 228.1255
## DMCC2165:AG4632 128.8945
## DMCC2165:DG47E80 201.5630
## DMCC2165:DG47X95 199.8590
## DMCC2165:Osage 81.3670
## DMCC2165:P5414LLS 102.5945
##
## $comparison
## NULL
##
## $groups
## ES13B$chl groups
## Control:Osage 236.66259 a
## Control:DG47E80 228.60578 a
## Control:DG47X95 217.34011 a
## Control:AG4632 190.99715 ab
## Control:P5414LLS 190.20437 abc
## DMCC2165:DG47X95 129.95467 bcd
## DMCC2165:DG47E80 120.54422 cd
## DMCC2165:AG4632 102.40559 d
## DMCC2165:P5414LLS 95.54374 d
## DMCC2165:Osage 89.13633 d
##
## attr(,"class")
## [1] "group"Same analysis as above using the tukey normalized dataset
#Statistical analysis
#####ES13B.mod###
ES13B.mod.chl.lm <- lm (ES13B.mod$ES13B_chl.tuk ~ ES13B.mod$Treatment +
ES13B.mod$HostVariety +
ES13B.mod$isoRepNumber +
ES13B.mod$techRepNumber +
ES13B.mod$SampleNumber)
ES13B.mod.chl.lm##
## Call:
## lm(formula = ES13B.mod$ES13B_chl.tuk ~ ES13B.mod$Treatment +
## ES13B.mod$HostVariety + ES13B.mod$isoRepNumber + ES13B.mod$techRepNumber +
## ES13B.mod$SampleNumber)
##
## Coefficients:
## (Intercept) ES13B.mod$TreatmentDMCC2165
## 56.4659 -27.1569
## ES13B.mod$HostVarietyDG47E80 ES13B.mod$HostVarietyDG47X95
## 6.8552 6.4268
## ES13B.mod$HostVarietyOsage ES13B.mod$HostVarietyP5414LLS
## 3.2278 -1.2888
## ES13B.mod$isoRepNumberisoRep2 ES13B.mod$isoRepNumberisoRep3
## -1.8503 0.1216
## ES13B.mod$techRepNumbertechRep2 ES13B.mod$techRepNumbertechRep3
## 7.5512 2.1409
## ES13B.mod$SampleNumbersample2 ES13B.mod$SampleNumbersample3
## 0.6429 0.7374summary(ES13B.mod.chl.lm)##
## Call:
## lm(formula = ES13B.mod$ES13B_chl.tuk ~ ES13B.mod$Treatment +
## ES13B.mod$HostVariety + ES13B.mod$isoRepNumber + ES13B.mod$techRepNumber +
## ES13B.mod$SampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -64.139 -11.806 0.251 11.105 58.266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 56.4659 4.4320 12.740 <2e-16 ***
## ES13B.mod$TreatmentDMCC2165 -27.1569 2.5754 -10.545 <2e-16 ***
## ES13B.mod$HostVarietyDG47E80 6.8552 4.0195 1.705 0.0893 .
## ES13B.mod$HostVarietyDG47X95 6.4268 4.1496 1.549 0.1227
## ES13B.mod$HostVarietyOsage 3.2278 4.0195 0.803 0.4227
## ES13B.mod$HostVarietyP5414LLS -1.2888 4.0195 -0.321 0.7488
## ES13B.mod$isoRepNumberisoRep2 -1.8503 3.1686 -0.584 0.5598
## ES13B.mod$isoRepNumberisoRep3 0.1216 3.1426 0.039 0.9692
## ES13B.mod$techRepNumbertechRep2 7.5512 3.1426 2.403 0.0170 *
## ES13B.mod$techRepNumbertechRep3 2.1409 3.1426 0.681 0.4963
## ES13B.mod$SampleNumbersample2 0.6429 3.1486 0.204 0.8384
## ES13B.mod$SampleNumbersample3 0.7374 3.1486 0.234 0.8150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.89 on 252 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.3339, Adjusted R-squared: 0.3048
## F-statistic: 11.48 on 11 and 252 DF, p-value: < 2.2e-16anova(ES13B.mod.chl.lm)## Analysis of Variance Table
##
## Response: ES13B.mod$ES13B_chl.tuk
## Df Sum Sq Mean Sq F value Pr(>F)
## ES13B.mod$Treatment 1 49427 49427 113.3105 < 2e-16 ***
## ES13B.mod$HostVariety 4 2794 698 1.6010 0.17455
## ES13B.mod$isoRepNumber 2 193 97 0.2216 0.80137
## ES13B.mod$techRepNumber 2 2663 1331 3.0519 0.04902 *
## ES13B.mod$SampleNumber 2 28 14 0.0325 0.96804
## Residuals 252 109925 436
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Tukey's HSD for Variable chl by Treatment
ES13B.mod.chl.treatment.HSD.test <- HSD.test(ES13B.mod.chl.lm, 'ES13B.mod$Treatment',
group = T)
ES13B.mod.chl.treatment.HSD.test## $statistics
## MSerror Df Mean CV
## 436.2119 252 49.24912 42.40824
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES13B.mod$Treatment 2 2.785184 0.05
##
## $means
## ES13B.mod$ES13B_chl.tuk std r Min Max Q25 Q50
## Control 62.62462 19.91280 135 0 104.34627 52.69278 65.58208
## DMCC2165 35.25151 22.09171 129 0 93.72814 21.23548 27.28194
## Q75
## Control 73.92184
## DMCC2165 51.52459
##
## $comparison
## NULL
##
## $groups
## ES13B.mod$ES13B_chl.tuk groups
## Control 62.62462 a
## DMCC2165 35.25151 b
##
## attr(,"class")
## [1] "group"#Tukey's HSD for Variable chl by Soybean Cultivar
ES13B.mod.chl.host_variety.HSD.test <- HSD.test(ES13B.mod.chl.lm,
'ES13B.mod$HostVariety', group = T)
ES13B.mod.chl.host_variety.HSD.test## $statistics
## MSerror Df Mean CV
## 436.2119 252 49.24912 42.40824
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES13B.mod$HostVariety 5 3.885737 0.05
##
## $means
## ES13B.mod$ES13B_chl.tuk std r Min Max Q25 Q50
## AG4632 46.00199 23.04112 54 0 96.69957 26.32493 44.64489
## DG47E80 52.85715 24.31248 54 0 98.36929 34.08532 61.52054
## DG47X95 53.96776 24.74253 48 0 95.60617 31.59813 58.99393
## Osage 49.22977 28.48843 54 0 104.34627 25.98636 49.51131
## P5414LLS 44.71323 23.92033 54 0 87.02381 23.82148 49.53890
## Q75
## AG4632 63.64317
## DG47E80 68.39115
## DG47X95 73.50975
## Osage 71.73382
## P5414LLS 65.74587
##
## $comparison
## NULL
##
## $groups
## ES13B.mod$ES13B_chl.tuk groups
## DG47X95 53.96776 a
## DG47E80 52.85715 a
## Osage 49.22977 a
## AG4632 46.00199 a
## P5414LLS 44.71323 a
##
## attr(,"class")
## [1] "group"#Complete ANOVA for ES13B.mod
ES13B.mod.comp.HSD.group <- HSD.test(ES13B.mod.chl.lm, c("ES13B.mod$Treatment",
"ES13B.mod$HostVariety"),
group=TRUE,console=TRUE)##
## Study: ES13B.mod.chl.lm ~ c("ES13B.mod$Treatment", "ES13B.mod$HostVariety")
##
## HSD Test for ES13B.mod$ES13B_chl.tuk
##
## Mean Square Error: 436.2119
##
## ES13B.mod$Treatment:ES13B.mod$HostVariety, means
##
## ES13B.mod.ES13B_chl.tuk std r Min Max
## Control:AG4632 57.20904 22.03662 27 0.00000 96.69957
## Control:DG47E80 66.70288 17.20548 27 35.38176 98.36929
## Control:DG47X95 64.01404 17.93361 27 24.66777 95.60617
## Control:Osage 67.15363 26.52535 27 0.00000 104.34627
## Control:P5414LLS 58.04351 11.99680 27 34.39165 76.51943
## DMCC2165:AG4632 34.79495 18.32309 27 0.00000 78.61163
## DMCC2165:DG47E80 39.01143 22.59966 27 0.00000 76.18307
## DMCC2165:DG47X95 41.05111 26.62931 21 0.00000 93.72814
## DMCC2165:Osage 31.30592 16.83924 27 0.00000 84.32030
## DMCC2165:P5414LLS 31.38296 25.56143 27 0.00000 87.02381
##
## Alpha: 0.05 ; DF Error: 252
## Critical Value of Studentized Range: 4.514628
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ES13B.mod$ES13B_chl.tuk groups
## Control:Osage 67.15363 a
## Control:DG47E80 66.70288 a
## Control:DG47X95 64.01404 a
## Control:P5414LLS 58.04351 ab
## Control:AG4632 57.20904 ab
## DMCC2165:DG47X95 41.05111 bc
## DMCC2165:DG47E80 39.01143 c
## DMCC2165:AG4632 34.79495 c
## DMCC2165:P5414LLS 31.38296 c
## DMCC2165:Osage 31.30592 cES13B.mod.comp.HSD.group## $statistics
## MSerror Df Mean CV
## 436.2119 252 49.24912 42.40824
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES13B.mod$Treatment:ES13B.mod$HostVariety 10 4.514628 0.05
##
## $means
## ES13B.mod$ES13B_chl.tuk std r Min Max
## Control:AG4632 57.20904 22.03662 27 0.00000 96.69957
## Control:DG47E80 66.70288 17.20548 27 35.38176 98.36929
## Control:DG47X95 64.01404 17.93361 27 24.66777 95.60617
## Control:Osage 67.15363 26.52535 27 0.00000 104.34627
## Control:P5414LLS 58.04351 11.99680 27 34.39165 76.51943
## DMCC2165:AG4632 34.79495 18.32309 27 0.00000 78.61163
## DMCC2165:DG47E80 39.01143 22.59966 27 0.00000 76.18307
## DMCC2165:DG47X95 41.05111 26.62931 21 0.00000 93.72814
## DMCC2165:Osage 31.30592 16.83924 27 0.00000 84.32030
## DMCC2165:P5414LLS 31.38296 25.56143 27 0.00000 87.02381
## Q25 Q50 Q75
## Control:AG4632 47.05388 62.96010 68.61805
## Control:DG47E80 62.26497 67.17475 78.30087
## Control:DG47X95 56.17982 65.54711 77.85944
## Control:Osage 65.40652 71.46612 76.90405
## Control:P5414LLS 51.98765 59.22541 67.22892
## DMCC2165:AG4632 22.34657 29.72913 43.18888
## DMCC2165:DG47E80 20.43572 33.65317 61.08024
## DMCC2165:DG47X95 19.88940 30.28659 60.68214
## DMCC2165:Osage 24.95960 26.04835 30.23822
## DMCC2165:P5414LLS 15.75931 23.21526 36.18682
##
## $comparison
## NULL
##
## $groups
## ES13B.mod$ES13B_chl.tuk groups
## Control:Osage 67.15363 a
## Control:DG47E80 66.70288 a
## Control:DG47X95 64.01404 a
## Control:P5414LLS 58.04351 ab
## Control:AG4632 57.20904 ab
## DMCC2165:DG47X95 41.05111 bc
## DMCC2165:DG47E80 39.01143 c
## DMCC2165:AG4632 34.79495 c
## DMCC2165:P5414LLS 31.38296 c
## DMCC2165:Osage 31.30592 c
##
## attr(,"class")
## [1] "group"Run analyses for ES14A
This dataset contains chlorophyll content measured among plant species treated with CFCFs from X. necrophora (isolate DMCC 2165) to estimate the specificy of SMs.
#####ES14A###
ES14A.chl.lm <- lm (ES14A$chl ~ ES14A$Treatment +
ES14A$Host + ES14A$isoRepNumber +
ES14A$techRepNumber +
ES14A$LeafSampleNumber)
ES14A.chl.lm##
## Call:
## lm(formula = ES14A$chl ~ ES14A$Treatment + ES14A$Host + ES14A$isoRepNumber +
## ES14A$techRepNumber + ES14A$LeafSampleNumber)
##
## Coefficients:
## (Intercept) ES14A$TreatmentDMCC2165
## 204.803 -39.317
## ES14A$HostPeanut ES14A$HostSoybean
## 71.821 -20.797
## ES14A$HostTomato ES14A$isoRepNumberisoRep2
## 20.597 8.076
## ES14A$isoRepNumberisoRep3 ES14A$techRepNumbertechRep2
## 10.061 -3.623
## ES14A$techRepNumbertechRep3 ES14A$LeafSampleNumbersample2
## -2.447 -2.221
## ES14A$LeafSampleNumbersample3
## -17.082summary(ES14A.chl.lm)##
## Call:
## lm(formula = ES14A$chl ~ ES14A$Treatment + ES14A$Host + ES14A$isoRepNumber +
## ES14A$techRepNumber + ES14A$LeafSampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -152.26 -25.67 3.28 28.37 140.22
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 204.803 11.492 17.821 < 2e-16 ***
## ES14A$TreatmentDMCC2165 -39.317 6.956 -5.652 5.34e-08 ***
## ES14A$HostPeanut 71.821 9.760 7.359 4.58e-12 ***
## ES14A$HostSoybean -20.797 9.760 -2.131 0.0343 *
## ES14A$HostTomato 20.597 9.914 2.078 0.0390 *
## ES14A$isoRepNumberisoRep2 8.076 8.552 0.944 0.3461
## ES14A$isoRepNumberisoRep3 10.061 8.452 1.190 0.2353
## ES14A$techRepNumbertechRep2 -3.623 8.552 -0.424 0.6723
## ES14A$techRepNumbertechRep3 -2.447 8.552 -0.286 0.7751
## ES14A$LeafSampleNumbersample2 -2.221 8.512 -0.261 0.7944
## ES14A$LeafSampleNumbersample3 -17.082 8.512 -2.007 0.0461 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 50.71 on 202 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.4051, Adjusted R-squared: 0.3756
## F-statistic: 13.75 on 10 and 202 DF, p-value: < 2.2e-16anova(ES14A.chl.lm)## Analysis of Variance Table
##
## Response: ES14A$chl
## Df Sum Sq Mean Sq F value Pr(>F)
## ES14A$Treatment 1 81494 81494 31.6869 6.003e-08 ***
## ES14A$Host 3 255475 85158 33.1116 < 2.2e-16 ***
## ES14A$isoRepNumber 2 4050 2025 0.7874 0.4564
## ES14A$techRepNumber 2 478 239 0.0930 0.9112
## ES14A$LeafSampleNumber 2 12250 6125 2.3815 0.0950 .
## Residuals 202 519515 2572
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Tukey's HSD for Variable chl by Treatment
ES14A.chl.treatment.HSD.test <- HSD.test(ES14A.chl.lm, 'ES14A$Treatment', group = T)
ES14A.chl.treatment.HSD.test## $statistics
## MSerror Df Mean CV
## 2571.854 202 200.2661 25.32304
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES14A$Treatment 2 2.788514 0.05
##
## $means
## ES14A$chl std r Min Max Q25 Q50 Q75
## Control 220.1037 48.30845 105 74.284 312.775 199.7190 220.323 244.5180
## DMCC2165 180.9794 71.63395 108 43.371 317.520 136.5077 190.138 227.8515
##
## $comparison
## NULL
##
## $groups
## ES14A$chl groups
## Control 220.1037 a
## DMCC2165 180.9794 b
##
## attr(,"class")
## [1] "group"#Tukey's HSD for Variable chl by Plant Species
ES14A.chl.host.HSD.test <- HSD.test(ES14A.chl.lm, 'ES14A$Host', group = T)
ES14A.chl.host.HSD.test## $statistics
## MSerror Df Mean CV
## 2571.854 202 200.2661 25.32304
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES14A$Host 4 3.663584 0.05
##
## $means
## ES14A$chl std r Min Max Q25 Q50 Q75
## Cotton 182.7328 41.22083 54 99.321 258.986 151.8048 189.3455 208.6360
## Peanut 254.5536 39.15515 54 104.832 317.520 232.0955 254.8250 282.4742
## Soybean 161.9354 88.07831 54 43.371 312.775 66.5095 174.5450 226.9425
## Tomato 201.9352 26.66869 51 117.923 244.624 187.7870 203.6790 219.5155
##
## $comparison
## NULL
##
## $groups
## ES14A$chl groups
## Peanut 254.5536 a
## Tomato 201.9352 b
## Cotton 182.7328 bc
## Soybean 161.9354 c
##
## attr(,"class")
## [1] "group"#Complete ANOVA for ES14A
ES14A.comp.HSD.group <- HSD.test(ES14A.chl.lm, c("ES14A$Treatment", "ES14A$Host"), group=TRUE,console=TRUE)##
## Study: ES14A.chl.lm ~ c("ES14A$Treatment", "ES14A$Host")
##
## HSD Test for ES14A$chl
##
## Mean Square Error: 2571.854
##
## ES14A$Treatment:ES14A$Host, means
##
## ES14A.chl std r Min Max
## Control:Cotton 194.11622 42.12477 27 106.098 254.411
## Control:Peanut 243.47885 43.34219 27 104.832 305.065
## Control:Soybean 226.62589 63.78820 27 74.284 312.775
## Control:Tomato 215.70517 17.85696 24 183.593 244.624
## DMCC2165:Cotton 171.34937 37.68338 27 99.321 258.986
## DMCC2165:Peanut 265.62833 31.49505 27 200.016 317.520
## DMCC2165:Soybean 97.24481 55.25735 27 43.371 210.220
## DMCC2165:Tomato 189.69526 27.47809 27 117.923 236.489
##
## Alpha: 0.05 ; DF Error: 202
## Critical Value of Studentized Range: 4.331714
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ES14A$chl groups
## DMCC2165:Peanut 265.62833 a
## Control:Peanut 243.47885 ab
## Control:Soybean 226.62589 abc
## Control:Tomato 215.70517 bc
## Control:Cotton 194.11622 cd
## DMCC2165:Tomato 189.69526 cd
## DMCC2165:Cotton 171.34937 d
## DMCC2165:Soybean 97.24481 eES14A.comp.HSD.group## $statistics
## MSerror Df Mean CV
## 2571.854 202 200.2661 25.32304
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES14A$Treatment:ES14A$Host 8 4.331714 0.05
##
## $means
## ES14A$chl std r Min Max Q25 Q50
## Control:Cotton 194.11622 42.12477 27 106.098 254.411 172.7065 201.2180
## Control:Peanut 243.47885 43.34219 27 104.832 305.065 220.0160 244.4330
## Control:Soybean 226.62589 63.78820 27 74.284 312.775 205.7205 227.9410
## Control:Tomato 215.70517 17.85696 24 183.593 244.624 203.4402 214.3875
## DMCC2165:Cotton 171.34937 37.68338 27 99.321 258.986 146.1180 180.5490
## DMCC2165:Peanut 265.62833 31.49505 27 200.016 317.520 247.1435 262.9750
## DMCC2165:Soybean 97.24481 55.25735 27 43.371 210.220 52.2970 66.4980
## DMCC2165:Tomato 189.69526 27.47809 27 117.923 236.489 178.0980 191.1460
## Q75
## Control:Cotton 229.7960
## Control:Peanut 274.2060
## Control:Soybean 274.5295
## Control:Tomato 227.4280
## DMCC2165:Cotton 198.6270
## DMCC2165:Peanut 290.1215
## DMCC2165:Soybean 143.0605
## DMCC2165:Tomato 206.2940
##
## $comparison
## NULL
##
## $groups
## ES14A$chl groups
## DMCC2165:Peanut 265.62833 a
## Control:Peanut 243.47885 ab
## Control:Soybean 226.62589 abc
## Control:Tomato 215.70517 bc
## Control:Cotton 194.11622 cd
## DMCC2165:Tomato 189.69526 cd
## DMCC2165:Cotton 171.34937 d
## DMCC2165:Soybean 97.24481 e
##
## attr(,"class")
## [1] "group"Same analysis as above using Tukey’s normalized data.
#####ES14A.mod.mod###
ES14A.mod.chl.lm <- lm (ES14A.mod$ES14A_chl.tuk ~ ES14A.mod$Treatment +
ES14A.mod$Host +
ES14A.mod$isoRepNumber +
ES14A.mod$techRepNumber +
ES14A.mod$LeafSampleNumber)
ES14A.mod.chl.lm##
## Call:
## lm(formula = ES14A.mod$ES14A_chl.tuk ~ ES14A.mod$Treatment +
## ES14A.mod$Host + ES14A.mod$isoRepNumber + ES14A.mod$techRepNumber +
## ES14A.mod$LeafSampleNumber)
##
## Coefficients:
## (Intercept) ES14A.mod$TreatmentDMCC2165
## 9573.32 -2709.06
## ES14A.mod$HostPeanut ES14A.mod$HostSoybean
## 6109.14 -562.35
## ES14A.mod$HostTomato ES14A.mod$isoRepNumberisoRep2
## 1457.89 752.80
## ES14A.mod$isoRepNumberisoRep3 ES14A.mod$techRepNumbertechRep2
## 707.59 -175.15
## ES14A.mod$techRepNumbertechRep3 ES14A.mod$LeafSampleNumbersample2
## -380.75 -57.52
## ES14A.mod$LeafSampleNumbersample3
## -831.24summary(ES14A.mod.chl.lm)##
## Call:
## lm(formula = ES14A.mod$ES14A_chl.tuk ~ ES14A.mod$Treatment +
## ES14A.mod$Host + ES14A.mod$isoRepNumber + ES14A.mod$techRepNumber +
## ES14A.mod$LeafSampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11413 -2124 40 2186 11598
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9573.32 861.86 11.108 < 2e-16 ***
## ES14A.mod$TreatmentDMCC2165 -2709.06 521.66 -5.193 5.03e-07 ***
## ES14A.mod$HostPeanut 6109.14 731.96 8.346 1.10e-14 ***
## ES14A.mod$HostSoybean -562.35 731.96 -0.768 0.4432
## ES14A.mod$HostTomato 1457.89 743.48 1.961 0.0513 .
## ES14A.mod$isoRepNumberisoRep2 752.80 641.39 1.174 0.2419
## ES14A.mod$isoRepNumberisoRep3 707.59 633.89 1.116 0.2656
## ES14A.mod$techRepNumbertechRep2 -175.15 641.39 -0.273 0.7851
## ES14A.mod$techRepNumbertechRep3 -380.75 641.39 -0.594 0.5534
## ES14A.mod$LeafSampleNumbersample2 -57.52 638.34 -0.090 0.9283
## ES14A.mod$LeafSampleNumbersample3 -831.24 638.34 -1.302 0.1943
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3803 on 202 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.3985, Adjusted R-squared: 0.3687
## F-statistic: 13.38 on 10 and 202 DF, p-value: < 2.2e-16anova(ES14A.mod.chl.lm)## Analysis of Variance Table
##
## Response: ES14A.mod$ES14A_chl.tuk
## Df Sum Sq Mean Sq F value Pr(>F)
## ES14A.mod$Treatment 1 389423237 389423237 26.9209 5.141e-07 ***
## ES14A.mod$Host 3 1485413072 495137691 34.2289 < 2.2e-16 ***
## ES14A.mod$isoRepNumber 2 25123911 12561955 0.8684 0.4212
## ES14A.mod$techRepNumber 2 5115841 2557921 0.1768 0.8381
## ES14A.mod$LeafSampleNumber 2 30598645 15299322 1.0576 0.3492
## Residuals 202 2922025050 14465471
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Tukey's HSD for Variable chl by Treatment
ES14A.mod.chl.treatment.HSD.test <- HSD.test(ES14A.mod.chl.lm, 'ES14A.mod$Treatment', group = T)
ES14A.mod.chl.treatment.HSD.test## $statistics
## MSerror Df Mean CV
## 14465471 202 9953.906 38.20962
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES14A.mod$Treatment 2 2.788514 0.05
##
## $means
## ES14A.mod$ES14A_chl.tuk std r Min Max Q25
## Control 11325.224 3958.923 105 1687.6965 20150.01 9294.444
## DMCC2165 8620.679 5150.335 108 667.0663 20680.22 4821.441
## Q50 Q75
## Control 11009.769 13177.60
## DMCC2165 8538.763 11666.87
##
## $comparison
## NULL
##
## $groups
## ES14A.mod$ES14A_chl.tuk groups
## Control 11325.224 a
## DMCC2165 8620.679 b
##
## attr(,"class")
## [1] "group"#Tukey's HSD for Variable chl by Plant Species
ES14A.mod.chl.host.HSD.test <- HSD.test(ES14A.mod.chl.lm, 'ES14A.mod$Host', group = T)
ES14A.mod.chl.host.HSD.test## $statistics
## MSerror Df Mean CV
## 14465471 202 9953.906 38.20962
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES14A.mod$Host 4 3.663584 0.05
##
## $means
## ES14A.mod$ES14A_chl.tuk std r Min Max Q25
## Cotton 8224.039 3055.399 54 2785.4532 14551.29 5790.517
## Peanut 14333.182 3553.092 54 3057.3977 20680.22 12044.169
## Soybean 7661.688 6130.187 54 667.0663 20150.01 1394.679
## Tomato 9575.703 2068.306 51 3745.4797 13187.45 8357.435
## Q50 Q75
## Cotton 8477.450 10021.99
## Peanut 14150.559 16902.50
## Soybean 7366.996 11586.99
## Tomato 9614.624 10940.26
##
## $comparison
## NULL
##
## $groups
## ES14A.mod$ES14A_chl.tuk groups
## Peanut 14333.182 a
## Tomato 9575.703 b
## Cotton 8224.039 b
## Soybean 7661.688 b
##
## attr(,"class")
## [1] "group"#Complete ANOVA for ES14A.mod
ES14A.mod.comp.HSD.group <- HSD.test(ES14A.mod.chl.lm, c("ES14A.mod$Treatment",
"ES14A.mod$Host"),
group=TRUE,console=TRUE)##
## Study: ES14A.mod.chl.lm ~ c("ES14A.mod$Treatment", "ES14A.mod$Host")
##
## HSD Test for ES14A.mod$ES14A_chl.tuk
##
## Mean Square Error: 14465471
##
## ES14A.mod$Treatment:ES14A.mod$Host, means
##
## ES14A.mod.ES14A_chl.tuk std r Min Max
## Control:Cotton 9103.740 3172.165 27 3121.3676 14110.73
## Control:Peanut 13336.356 3754.679 27 3057.3977 19300.87
## Control:Soybean 12128.331 5109.049 27 1687.6965 20150.01
## Control:Tomato 10658.376 1512.759 24 8038.0704 13187.45
## DMCC2165:Cotton 7344.338 2712.946 27 2785.4532 14551.29
## DMCC2165:Peanut 15330.007 3094.045 27 9318.2997 20680.22
## DMCC2165:Soybean 3195.045 3010.793 27 667.0663 10153.43
## DMCC2165:Tomato 8613.327 2039.245 27 3745.4797 12440.10
##
## Alpha: 0.05 ; DF Error: 202
## Critical Value of Studentized Range: 4.331714
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ES14A.mod$ES14A_chl.tuk groups
## DMCC2165:Peanut 15330.007 a
## Control:Peanut 13336.356 ab
## Control:Soybean 12128.331 bc
## Control:Tomato 10658.376 bcd
## Control:Cotton 9103.740 cde
## DMCC2165:Tomato 8613.327 de
## DMCC2165:Cotton 7344.338 e
## DMCC2165:Soybean 3195.045 fES14A.mod.comp.HSD.group## $statistics
## MSerror Df Mean CV
## 14465471 202 9953.906 38.20962
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES14A.mod$Treatment:ES14A.mod$Host 8 4.331714 0.05
##
## $means
## ES14A.mod$ES14A_chl.tuk std r Min Max
## Control:Cotton 9103.740 3172.165 27 3121.3676 14110.73
## Control:Peanut 13336.356 3754.679 27 3057.3977 19300.87
## Control:Soybean 12128.331 5109.049 27 1687.6965 20150.01
## Control:Tomato 10658.376 1512.759 24 8038.0704 13187.45
## DMCC2165:Cotton 7344.338 2712.946 27 2785.4532 14551.29
## DMCC2165:Peanut 15330.007 3094.045 27 9318.2997 20680.22
## DMCC2165:Soybean 3195.045 3010.793 27 667.0663 10153.43
## DMCC2165:Tomato 8613.327 2039.245 27 3745.4797 12440.10
## Q25 Q50 Q75
## Control:Cotton 7235.4241 9415.107 11840.543
## Control:Peanut 10983.3242 13169.695 16057.689
## Control:Soybean 9787.4152 11674.646 16090.596
## Control:Tomato 9595.2157 10503.255 11629.391
## DMCC2165:Cotton 5421.6757 7809.559 9207.086
## DMCC2165:Peanut 13422.7747 14940.064 17699.067
## DMCC2165:Soybean 921.2485 1394.263 5240.336
## DMCC2165:Tomato 7629.0246 8616.978 9828.773
##
## $comparison
## NULL
##
## $groups
## ES14A.mod$ES14A_chl.tuk groups
## DMCC2165:Peanut 15330.007 a
## Control:Peanut 13336.356 ab
## Control:Soybean 12128.331 bc
## Control:Tomato 10658.376 bcd
## Control:Cotton 9103.740 cde
## DMCC2165:Tomato 8613.327 de
## DMCC2165:Cotton 7344.338 e
## DMCC2165:Soybean 3195.045 f
##
## attr(,"class")
## [1] "group"Statistical analyses for root lenght
Loading dataset (root growth)
ES2.root <- read.csv("../raw_data/ES2.rootMeasurements.csv", header = T)
#Clean dataset for plotting and analyses
ES2.root.noNAs <- na.omit(ES2.root)Initial statistical analysis (raw data)
#ES2 longest root statistical analysis
ES2.root.noNAs.lm <- lm (ES2.root.noNAs$Length ~ ES2.root.noNAs$Isolate + ES2.root.noNAs$Condition + ES2.root.noNAs$Concentration, na.action=na.exclude)
ES2.root.noNAs.lm##
## Call:
## lm(formula = ES2.root.noNAs$Length ~ ES2.root.noNAs$Isolate +
## ES2.root.noNAs$Condition + ES2.root.noNAs$Concentration,
## na.action = na.exclude)
##
## Coefficients:
## (Intercept) ES2.root.noNAs$IsolateDMCC2126
## 38.608 -10.916
## ES2.root.noNAs$IsolateDMCC2127 ES2.root.noNAs$IsolateDMCC2165
## -8.786 -12.099
## ES2.root.noNAs$IsolateDMCC2966 ES2.root.noNAs$ConditionStationary
## 13.649 -6.885
## ES2.root.noNAs$Concentration25fold
## -25.132summary(ES2.root.noNAs.lm)##
## Call:
## lm(formula = ES2.root.noNAs$Length ~ ES2.root.noNAs$Isolate +
## ES2.root.noNAs$Condition + ES2.root.noNAs$Concentration,
## na.action = na.exclude)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.264 -8.173 1.284 7.818 22.674
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.608 3.659 10.550 3.33e-15 ***
## ES2.root.noNAs$IsolateDMCC2126 -10.916 5.457 -2.000 0.05008 .
## ES2.root.noNAs$IsolateDMCC2127 -8.786 5.223 -1.682 0.09781 .
## ES2.root.noNAs$IsolateDMCC2165 -12.099 4.986 -2.427 0.01832 *
## ES2.root.noNAs$IsolateDMCC2966 13.649 4.199 3.250 0.00191 **
## ES2.root.noNAs$ConditionStationary -6.885 3.178 -2.167 0.03431 *
## ES2.root.noNAs$Concentration25fold -25.132 3.492 -7.197 1.26e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.49 on 59 degrees of freedom
## Multiple R-squared: 0.5919, Adjusted R-squared: 0.5504
## F-statistic: 14.26 on 6 and 59 DF, p-value: 5.795e-10anova(ES2.root.noNAs.lm)## Analysis of Variance Table
##
## Response: ES2.root.noNAs$Length
## Df Sum Sq Mean Sq F value Pr(>F)
## ES2.root.noNAs$Isolate 4 4955.4 1238.8 7.9369 3.450e-05 ***
## ES2.root.noNAs$Condition 1 317.6 317.6 2.0349 0.159
## ES2.root.noNAs$Concentration 1 8084.1 8084.1 51.7926 1.256e-09 ***
## Residuals 59 9209.1 156.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Tukey's HSD for Variable Condition
ES2.root.noNAs.condition.HSD.test <- HSD.test(ES2.root.noNAs.lm,
'ES2.root.noNAs$Condition', group = T)
ES2.root.noNAs.condition.HSD.test## $statistics
## MSerror Df Mean CV
## 156.086 59 26.46406 47.20907
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.root.noNAs$Condition 2 2.829835 0.05
##
## $means
## ES2.root.noNAs$Length std r Min Max Q25 Q50 Q75
## Shaking 27.54116 19.14552 37 0.759 67.578 14.983 24.544 36.420
## Stationary 25.08983 18.19797 29 0.982 68.045 13.602 17.404 38.714
##
## $comparison
## NULL
##
## $groups
## ES2.root.noNAs$Length groups
## Shaking 27.54116 a
## Stationary 25.08983 a
##
## attr(,"class")
## [1] "group"#Tukey's HSD for Variable Concentration
ES2.root.noNAs.Concentration.HSD.test <- HSD.test(ES2.root.noNAs.lm, 'ES2.root.noNAs$Concentration', group = T)
ES2.root.noNAs.Concentration.HSD.test## $statistics
## MSerror Df Mean CV
## 156.086 59 26.46406 47.20907
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.root.noNAs$Concentration 2 2.829835 0.05
##
## $means
## ES2.root.noNAs$Length std r Min Max Q25 Q50 Q75
## 100fold 33.41979 18.02719 43 2.261 68.045 16.7635 31.069 47.0615
## 25fold 13.45987 11.57407 23 0.759 38.442 1.8595 14.252 19.0160
##
## $comparison
## NULL
##
## $groups
## ES2.root.noNAs$Length groups
## 100fold 33.41979 a
## 25fold 13.45987 b
##
## attr(,"class")
## [1] "group"#Tukey's HSD for Variable Isolate
ES2.root.noNAs.isolate.HSD.test <- HSD.test(ES2.root.noNAs.lm, 'ES2.root.noNAs$Isolate', group = T)
ES2.root.noNAs.isolate.HSD.test## $statistics
## MSerror Df Mean CV
## 156.086 59 26.46406 47.20907
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.root.noNAs$Isolate 5 3.97949 0.05
##
## $means
## ES2.root.noNAs$Length std r Min Max Q25 Q50 Q75
## Control 25.46106 14.42338 16 0.759 53.277 15.1875 21.304 32.42575
## DMCC2126 23.86656 15.08114 9 2.261 43.013 13.8810 28.594 35.49300
## DMCC2127 13.56456 13.67932 9 1.131 36.420 1.7050 15.283 15.82100
## DMCC2165 18.80955 13.95768 11 0.885 46.821 10.3030 15.075 27.10500
## DMCC2966 37.87933 21.47743 21 0.982 68.045 24.5440 33.212 58.57400
##
## $comparison
## NULL
##
## $groups
## ES2.root.noNAs$Length groups
## DMCC2966 37.87933 a
## Control 25.46106 b
## DMCC2126 23.86656 b
## DMCC2165 18.80955 b
## DMCC2127 13.56456 b
##
## attr(,"class")
## [1] "group"#Tukey's HSD for Treatment and concentration
ES2.root.noNAs.leafsec.treat.dil.HSD.test <- HSD.test(ES2.root.noNAs.lm, c('ES2.root.noNAs$Isolate', 'ES2.root.noNAs$Concentration'), group = T )
ES2.root.noNAs.leafsec.treat.dil.HSD.test## $statistics
## MSerror Df Mean CV
## 156.086 59 26.46406 47.20907
##
## $parameters
## test name.t ntr
## Tukey ES2.root.noNAs$Isolate:ES2.root.noNAs$Concentration 9
## StudentizedRange alpha
## 4.55324 0.05
##
## $means
## ES2.root.noNAs$Length std r Min Max Q25
## Control:100fold 34.51244 12.257238 9 19.375 53.277 26.42600
## Control:25fold 13.82357 6.234620 7 0.759 20.628 13.92700
## DMCC2126:100fold 23.86656 15.081139 9 2.261 43.013 13.88100
## DMCC2127:100fold 25.15625 11.174660 4 15.283 36.420 15.68650
## DMCC2127:25fold 4.29120 6.223480 5 1.131 15.405 1.20100
## DMCC2165:100fold 22.60056 12.426130 9 7.425 46.821 14.98300
## DMCC2165:25fold 1.75000 1.223295 2 0.885 2.615 1.31750
## DMCC2966:100fold 50.63417 17.328417 12 15.108 68.045 43.24375
## DMCC2966:25fold 20.87289 13.073765 9 0.982 38.442 13.88400
## Q50 Q75
## Control:100fold 30.2620 41.43500
## Control:25fold 14.8050 16.35950
## DMCC2126:100fold 28.5940 35.49300
## DMCC2127:100fold 24.4610 33.93075
## DMCC2127:25fold 1.7050 2.01400
## DMCC2165:100fold 17.7060 30.29700
## DMCC2165:25fold 1.7500 2.18250
## DMCC2966:100fold 55.6675 64.10850
## DMCC2966:25fold 24.5440 29.70700
##
## $comparison
## NULL
##
## $groups
## ES2.root.noNAs$Length groups
## DMCC2966:100fold 50.63417 a
## Control:100fold 34.51244 ab
## DMCC2127:100fold 25.15625 bc
## DMCC2126:100fold 23.86656 bc
## DMCC2165:100fold 22.60056 bc
## DMCC2966:25fold 20.87289 bc
## Control:25fold 13.82357 c
## DMCC2127:25fold 4.29120 c
## DMCC2165:25fold 1.75000 c
##
## attr(,"class")
## [1] "group"Comparison after normalization of data
# Used the same Tukey's normalization methods used above
ES2.root.tuk = transformTukey(ES2.root.noNAs$Length, plotit=FALSE)##
## lambda W Shapiro.p.value
## 427 0.65 0.964 0.0525
##
## if (lambda > 0){TRANS = x ^ lambda}
## if (lambda == 0){TRANS = log(x)}
## if (lambda < 0){TRANS = -1 * x ^ lambda}ES2.root.noNAs.mod = cbind(ES2.root.noNAs, ES2.root.tuk)
#ES2 longest root statistical analysis after normalization
ES2.root.noNAs.mod.lm <- lm (ES2.root.noNAs.mod$ES2.root.tuk ~ ES2.root.noNAs.mod$Isolate +
ES2.root.noNAs.mod$Condition +
ES2.root.noNAs.mod$Concentration, na.action=na.exclude)
ES2.root.noNAs.mod.lm##
## Call:
## lm(formula = ES2.root.noNAs.mod$ES2.root.tuk ~ ES2.root.noNAs.mod$Isolate +
## ES2.root.noNAs.mod$Condition + ES2.root.noNAs.mod$Concentration,
## na.action = na.exclude)
##
## Coefficients:
## (Intercept) ES2.root.noNAs.mod$IsolateDMCC2126
## 10.769 -2.553
## ES2.root.noNAs.mod$IsolateDMCC2127 ES2.root.noNAs.mod$IsolateDMCC2165
## -2.390 -2.826
## ES2.root.noNAs.mod$IsolateDMCC2966 ES2.root.noNAs.mod$ConditionStationary
## 2.501 -1.414
## ES2.root.noNAs.mod$Concentration25fold
## -5.617summary(ES2.root.noNAs.mod.lm)##
## Call:
## lm(formula = ES2.root.noNAs.mod$ES2.root.tuk ~ ES2.root.noNAs.mod$Isolate +
## ES2.root.noNAs.mod$Condition + ES2.root.noNAs.mod$Concentration,
## na.action = na.exclude)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.015 -1.626 0.381 1.994 4.728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.7694 0.7898 13.635 < 2e-16 ***
## ES2.root.noNAs.mod$IsolateDMCC2126 -2.5526 1.1779 -2.167 0.0343 *
## ES2.root.noNAs.mod$IsolateDMCC2127 -2.3895 1.1273 -2.120 0.0382 *
## ES2.root.noNAs.mod$IsolateDMCC2165 -2.8263 1.0762 -2.626 0.0110 *
## ES2.root.noNAs.mod$IsolateDMCC2966 2.5010 0.9064 2.759 0.0077 **
## ES2.root.noNAs.mod$ConditionStationary -1.4140 0.6859 -2.062 0.0437 *
## ES2.root.noNAs.mod$Concentration25fold -5.6168 0.7537 -7.452 4.64e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.697 on 59 degrees of freedom
## Multiple R-squared: 0.5978, Adjusted R-squared: 0.5569
## F-statistic: 14.61 on 6 and 59 DF, p-value: 3.856e-10anova(ES2.root.noNAs.mod.lm)## Analysis of Variance Table
##
## Response: ES2.root.noNAs.mod$ES2.root.tuk
## Df Sum Sq Mean Sq F value Pr(>F)
## ES2.root.noNAs.mod$Isolate 4 221.55 55.39 7.6175 5.116e-05 ***
## ES2.root.noNAs.mod$Condition 1 12.18 12.18 1.6751 0.2006
## ES2.root.noNAs.mod$Concentration 1 403.79 403.79 55.5332 4.637e-10 ***
## Residuals 59 429.00 7.27
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Tukey's HSD for Variable Condition
ES2.root.noNAs.mod.condition.HSD.test <- HSD.test(ES2.root.noNAs.mod.lm, 'ES2.root.noNAs.mod$Condition', group = T)
ES2.root.noNAs.mod.condition.HSD.test## $statistics
## MSerror Df Mean CV
## 7.271182 59 7.841521 34.38763
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.root.noNAs.mod$Condition 2 2.829835 0.05
##
## $means
## ES2.root.noNAs.mod$ES2.root.tuk std r Min Max
## Shaking 8.046515 4.162235 37 0.8359054 15.46584
## Stationary 7.579976 3.961030 29 0.9882628 15.53522
## Q25 Q50 Q75
## Shaking 5.809506 8.006901 10.34835
## Stationary 5.455591 6.403566 10.76748
##
## $comparison
## NULL
##
## $groups
## ES2.root.noNAs.mod$ES2.root.tuk groups
## Shaking 8.046515 a
## Stationary 7.579976 a
##
## attr(,"class")
## [1] "group"#Tukey's HSD for Variable Concentration
ES2.root.noNAs.mod.Concentration.HSD.test <- HSD.test(ES2.root.noNAs.mod.lm, 'ES2.root.noNAs.mod$Concentration', group = T)
ES2.root.noNAs.mod.Concentration.HSD.test## $statistics
## MSerror Df Mean CV
## 7.271182 59 7.841521 34.38763
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.root.noNAs.mod$Concentration 2 2.829835 0.05
##
## $means
## ES2.root.noNAs.mod$ES2.root.tuk std r Min Max Q25
## 100fold 9.428687 3.551457 43 1.6993990 15.53522 6.247133
## 25fold 4.874211 3.204748 23 0.8359054 10.71825 1.495429
## Q50 Q75
## 100fold 9.332817 12.224510
## 25fold 5.623663 6.777533
##
## $comparison
## NULL
##
## $groups
## ES2.root.noNAs.mod$ES2.root.tuk groups
## 100fold 9.428687 a
## 25fold 4.874211 b
##
## attr(,"class")
## [1] "group"#Tukey's HSD for Variable Isolate
ES2.root.noNAs.mod.isolate.HSD.test <- HSD.test(ES2.root.noNAs.mod.lm, 'ES2.root.noNAs.mod$Isolate', group = T)
ES2.root.noNAs.mod.isolate.HSD.test## $statistics
## MSerror Df Mean CV
## 7.271182 59 7.841521 34.38763
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.root.noNAs.mod$Isolate 5 3.97949 0.05
##
## $means
## ES2.root.noNAs.mod$ES2.root.tuk std r Min Max
## Control 7.870193 3.154754 16 0.8359054 13.25107
## DMCC2126 7.431287 3.579335 9 1.6993990 11.53028
## DMCC2127 4.788109 3.676082 9 1.0833049 10.34835
## DMCC2165 6.279095 3.393188 11 0.9236621 12.18390
## DMCC2966 10.122508 4.300847 21 0.9882628 15.53522
## Q25 Q50 Q75
## Control 5.860782 7.302137 9.581929
## DMCC2126 5.528069 8.842574 10.176367
## DMCC2127 1.414558 5.884853 6.018691
## DMCC2165 4.513060 5.832668 8.526954
## DMCC2966 8.006901 9.746345 14.093153
##
## $comparison
## NULL
##
## $groups
## ES2.root.noNAs.mod$ES2.root.tuk groups
## DMCC2966 10.122508 a
## Control 7.870193 ab
## DMCC2126 7.431287 ab
## DMCC2165 6.279095 b
## DMCC2127 4.788109 b
##
## attr(,"class")
## [1] "group"#Tukey's HSD for Treatment and concentration
ES2.root.noNAs.mod.leafsec.treat.dil.HSD.test <- HSD.test(ES2.root.noNAs.mod.lm, c('ES2.root.noNAs.mod$Isolate', 'ES2.root.noNAs.mod$Concentration'), group = T )
ES2.root.noNAs.mod.leafsec.treat.dil.HSD.test## $statistics
## MSerror Df Mean CV
## 7.271182 59 7.841521 34.38763
##
## $parameters
## test name.t ntr
## Tukey ES2.root.noNAs.mod$Isolate:ES2.root.noNAs.mod$Concentration 9
## StudentizedRange alpha
## 4.55324 0.05
##
## $means
## ES2.root.noNAs.mod$ES2.root.tuk std r Min
## Control:100fold 9.866162 2.2929937 9 6.8660524
## Control:25fold 5.303948 2.0522425 7 0.8359054
## DMCC2126:100fold 7.431287 3.5793348 9 1.6993990
## DMCC2127:100fold 7.994262 2.3727767 4 5.8848527
## DMCC2127:25fold 2.223187 2.0740415 5 1.0833049
## DMCC2165:100fold 7.364275 2.6551730 9 3.6808888
## DMCC2165:25fold 1.395787 0.6676861 2 0.9236621
## DMCC2966:100fold 12.625081 3.0906845 12 5.8409641
## DMCC2966:25fold 6.785744 3.3449518 9 0.9882628
## Max Q25 Q50 Q75
## Control:100fold 13.251067 8.400796 9.174522 11.253530
## Control:25fold 7.151500 5.539627 5.764551 6.148213
## DMCC2126:100fold 11.530279 5.528069 8.842574 10.176367
## DMCC2127:100fold 10.348346 5.985232 7.871925 9.880956
## DMCC2127:25fold 5.915345 1.126427 1.414558 1.576299
## DMCC2165:100fold 12.183903 5.809506 6.475574 9.181418
## DMCC2165:25fold 1.867913 1.159725 1.395787 1.631850
## DMCC2966:100fold 15.535225 11.532042 13.630329 14.944422
## DMCC2966:25fold 10.718250 5.528846 8.006901 9.064800
##
## $comparison
## NULL
##
## $groups
## ES2.root.noNAs.mod$ES2.root.tuk groups
## DMCC2966:100fold 12.625081 a
## Control:100fold 9.866162 ab
## DMCC2127:100fold 7.994262 abc
## DMCC2126:100fold 7.431287 bc
## DMCC2165:100fold 7.364275 bc
## DMCC2966:25fold 6.785744 bc
## Control:25fold 5.303948 c
## DMCC2127:25fold 2.223187 c
## DMCC2165:25fold 1.395787 c
##
## attr(,"class")
## [1] "group"Plotting individual plots and composite figures
Individual plots for figure 1
Extract the information needed for panel “A”
##Extract all control (ES5: 7 DOE)
ES5.control <- subset(ES5.mod, Treatment== "control")
ES5.Xn <- subset(ES5.mod, Treatment== c("DMCC2126", "DMCC2127", "DMCC2165"))
ES5.control <- ES5.control %>%
add_column(Species = "control")
ES5.Xn <- ES5.Xn %>%
add_column(Species = "X.necrophora")
ES5.mod.v2 <- rbind(ES5.control, ES5.Xn)
ES5.mod.ggplot <- ggplot(ES5.mod.v2, aes(x = reorder(Species, -chl, na.rm = TRUE),
y = chl, fill = Species)) +
geom_boxplot() + #geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
#scale_fill_grey(start = 1, end = 0.4) + labs(tag = "A") +
scale_fill_manual(values = c("#FFFFFF", "#545454"))+ labs(tag = "A") +
xlab("Treatment") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
facet_wrap(~ Dilution)
ES5.mod.ggplot## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
Individual plot for panel B
##Extract all control (ES2), colletrichum, and X. necrophora
ES2.control <- subset(ES2.mod, Treatment== "control")
ES2.coll <- subset(ES2.mod, Treatment== "DMCC2966")
ES2.Xn <- subset(ES2.mod, Treatment== c("DMCC2126", "DMCC2127", "DMCC2165"))
ES2.control <- ES2.control %>%
add_column(Species = "control")
ES2.coll <- ES2.coll %>%
add_column(Species = "C.siamense")
ES2.Xn <- ES2.Xn %>%
add_column(Species = "X.necrophora")
ES2.mod.v2 <- rbind(ES2.control, ES2.coll, ES2.Xn)
#plot for figure by species by dilution factor
#Reorganizing for plotting
ES2.mod.v2$Species <- factor(ES2.mod.v2$Species,
levels = c("control", "C.siamense", "X.necrophora"))
#Plot with ggplot
ES2.mod.v2.ggplot <- ggplot(ES2.mod.v2, aes(x = reorder(Species, -chl, na.rm = TRUE),
y = chl, fill = Species)) + geom_boxplot() + #geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
# scale_fill_grey("control" = 1, "C.siamense" =0.7, "X.necrophora"= 0.4)
scale_fill_manual(values = c("#FFFFFF", "#AAAAAA", "#545454"))+ labs(tag = "B") +
xlab("Treatment") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
facet_wrap(~ Dilution)
ES2.mod.v2.ggplot## Warning: Removed 32 rows containing non-finite values (stat_boxplot).
Individual panel C
#ES5 by treatment by dilution, no title
ES5.mod.ggplot.v2 <- ggplot(ES5.mod, aes(x = reorder(Treatment, -chl, na.rm = TRUE),
y = chl, fill = Dilution)) +
geom_boxplot() + #geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
scale_fill_grey(start = 1, end = 0.4) + labs(tag = "C") +
xlab("Treatment") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
facet_wrap(~ Condition)
ES5.mod.ggplot.v2## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
#Reorder for visualization purposes
ES5.mod.new <- ES5.mod
ES5.mod.new$Condition <- factor(ES5.mod.new$Condition, # Reordering group factor levels
levels = c("Stationary", "Shaking"))
#Plot reordered factors
ES5.mod.ggplot.v3 <- ggplot(ES5.mod.new, aes(x = reorder(Treatment, -chl, na.rm = TRUE),
y = chl, fill = Dilution)) +
geom_boxplot() + #geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
scale_fill_grey(start = 1, end = 0.4) + labs(tag = "C") +
xlab("Treatment") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
facet_wrap(.~ Condition)
ES5.mod.ggplot.v3## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
Individual panel D
#ES2 by treatment by dilution, by growth conditions no title
ES2.mod.ggplot.v2 <- ggplot(ES2.mod, aes(x = reorder(Treatment, -chl, na.rm = TRUE),
y = chl, fill = Dilution)) +
geom_boxplot() + #geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
scale_fill_grey(start = 1, end = 0.4) + labs(tag = "D") +
xlab("Treatment") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
ES2.mod.ggplot.v2## Warning: Removed 60 rows containing non-finite values (stat_boxplot).
Plot composite figure 1
###Grid for composite figure 1 (updated 05/11/2022). Using ES2 and ES5 only.
gridExtra::grid.arrange(ES5.mod.ggplot, ES2.mod.v2.ggplot, ES5.mod.ggplot.v3, ES2.mod.ggplot.v2, ncol=2)## Warning: Removed 8 rows containing non-finite values (stat_boxplot).## Warning: Removed 32 rows containing non-finite values (stat_boxplot).## Warning: Removed 12 rows containing non-finite values (stat_boxplot).## Warning: Removed 60 rows containing non-finite values (stat_boxplot).
Plotting root length panel (Figure 2)
#Plate for Figure 2 FINAL (USE THIS ONE, because no differences between Shaking and stat were observed)
ES2.root.noNAs.mod$Isolate <- with(ES2.root.noNAs.mod, reorder(Isolate, -Length))
ES2.root.noNAs.mod.ggplot.plate <- ggplot(ES2.root.noNAs.mod, aes(x = Isolate,
y = Length,
fill = Isolate)) +
geom_boxplot() +
#scale_fill_grey(start = 1, end = 0.4) +
#scale_fill_manual(values = c("Control"="green", "DMCC2966"="green", "DMCC2126"="gold", "DMCC2165"="gold", "DMCC2127"="gold"))+
#ggtitle("Root Length at 14 Days After Exposure") +
scale_fill_manual(values = c("#000000", "#FFFFFF", "#DADADA", "#ACACAC","#666666")) + #labs(tag = "") +
xlab("Treatment") + ylab("Root Length (mm)") +
theme(plot.title = element_text(size = 14, hjust = 0.5, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 30, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
facet_wrap(~ Concentration)
ES2.root.noNAs.mod.ggplot.plate
#dev.off()Plotting individual plots and composite figure 4
Individual panels A, B, C, and D.
###Plot HostVariety only w/ outliers
ES13B.ByHosCult <- ggplot(ES13B.mod, aes(x = reorder(HostVariety, -chl, na.rm = TRUE),
y = chl, fill=HostVariety)) +
geom_boxplot() +
scale_fill_grey(start = 1, end = 0.4) + labs(tag = "A") +
xlab("Soybean Cultivar") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
ES13B.ByHosCult## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
Individual panel B
###Plot by variety by treatment w/ outliers
ES13B.ggplot.ByCultByTreat <- ggplot(ES13B.mod, aes(x = reorder(HostVariety, -chl,
na.rm = TRUE),
y = chl, fill=Treatment)) +
geom_boxplot() + #+ geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
scale_fill_grey(start = 1, end = 0.4) + labs(tag = "B") +
xlab("Soybean Cultivar") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y =element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
ES13B.ggplot.ByCultByTreat## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
Panel C
###Plot By Host only w/ outliers for grid
ES14A.ggplot.ByHost <- ggplot(ES14A.mod, aes(x = reorder(Host, -chl, na.rm = TRUE),
y = chl, fill=Host)) +
geom_boxplot() + #+ geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
scale_fill_grey(start = 1, end = 0.4) + labs(tag = "C") +
xlab("Plant Species") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
ES14A.ggplot.ByHost ## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
Panel D
###Plot by host by treatment w/ outliers
ES14A.ggplot.ByHostByTreat <- ggplot(ES14A.mod, aes(x = reorder(Host, -chl, na.rm = TRUE),
y = chl, fill=Treatment)) +
geom_boxplot() + #+ geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
scale_fill_grey(start = 1, end = 0.4) + labs(tag = "D") +
xlab("Plant Species") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
ES14A.ggplot.ByHostByTreat ## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
Composite figure 4
###Grid for composite figure 4 (05/14/2022). Using ES13B and ES14 only.
gridExtra::grid.arrange(ES13B.ByHosCult,
ES13B.ggplot.ByCultByTreat ,
ES14A.ggplot.ByHost,
ES14A.ggplot.ByHostByTreat, ncol=2)## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
## Removed 6 rows containing non-finite values (stat_boxplot).## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
## Removed 3 rows containing non-finite values (stat_boxplot).
Supplementary Materials/Figures
Plotting Supplementary Figure 1
This composite figure contains the comparisons between fermentation conditions (shaking vs stationary) and dilutions (25 vs 100-fold) per strains.
#Panel A
#ES5 by conditions (side by side)
ES5.mod.ggplot.v3 <- ggplot(ES5.mod, aes(x = reorder(Condition, -chl, na.rm = TRUE),
y = chl, fill=Treatment)) +
geom_boxplot() +
scale_fill_grey(start =1, end = 0.4) + labs(tag = "A") +
xlab("Growth Condition") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
ES5.mod.ggplot.v3 ## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
#Panel B
#ES5 by dilutions (side by side)
ES5.mod.ggplot.v4 <- ggplot(ES5.mod, aes(x = reorder(Dilution, -chl, na.rm = TRUE),
y = chl, fill=Treatment)) +
geom_boxplot() +
scale_fill_grey(start =1, end = 0.4) + labs(tag = "B") +
xlab("Dilution Factor") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
ES5.mod.ggplot.v4## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
###Grid for supplementary figure 1 (updated 05/14/2022). Using ES2 and ES5 only.
gridExtra::grid.arrange(ES5.mod.ggplot.v3,
ES5.mod.ggplot.v4,
ncol=2)## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
## Removed 12 rows containing non-finite values (stat_boxplot).
Plotting Supplementary Figure 2
This composite figure contained validation chlorophyll content (chemical vs digital extractions) on panel A, fungal biomass based on Whatmat No 1 filter weight on panel B, measurements of pH from initial potato dextrose broth and filtered stock cell-free culture filtrates (CFCFs) on panel C, and the pearson correlation between final pH and digital chlorophyll content on panel D.
Loading datasets for composite figure
#Load datasets
ES10.chem <- read.csv("../raw_data/ES10.chem.chl.csv", header = T)
#Chlorophyll content obtained chemically for a dataset with all biomass and pH measurements
ES10.digital <- read.csv("../raw_data/ES10.digital.chl.csv", header = T)
#Chlorophyll content obtained digitally for a dataset with all biomass and pH measurements
BiomassAndpH.metadata <- read.csv("../raw_data/FilteringTreatments.metadata.csv",
header = T)Summarizing and aggregating datasets
#Obtaining sums for ES10 because digital measurements=3 per experimental unit,
#but chemical measurements=1 per experimental unit.
ES10.digital.sum <- aggregate(ES10.digital$chl,list(ES10.digital$ExpCode),sum)
names(ES10.digital.sum)[names(ES10.digital.sum) == "x"] <- "dig.chl"
#Merging ES10 chem and ES10 digital
ES10.chem.dig = merge(ES10.chem, ES10.digital.sum, by.x='ExpCode', by.y='Group.1')#Pearson correlations for ES10
cor(ES10.chem.dig$chl, ES10.chem.dig$dig.chl, method="pearson")## [1] 0.8450695Plotting Supplementary Figure 2 panel A
ES10.chem.dig.ggplot <- ggscatter(ES10.chem.dig, x = "chl", y = "dig.chl",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE,
cor.method = "pearson",
xlab = "Chemical chlorophyll content (ng/sq mm)",
ylab = "Digital chlorophyll content (ng/sq mm)") +
labs(tag = "A")
ES10.chem.dig.ggplot## `geom_smooth()` using formula 'y ~ x'
Plotting Biomass by Treatment by Condition
(Supplementary Figure 2, Panel B)
# Supplementary figure 2 panel B
## ES5 by dilutions (side by side)
BiomassAndpH.metadata.ggplot.B <- ggplot(BiomassAndpH.metadata,
aes(x = reorder(Isolate, +Weight_grams),
y = Weight_grams, fill=FilterWeight)) +
geom_boxplot() + #geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
scale_fill_grey(start =0.4, end = 1) + labs(tag = "B") +
xlab("Treatment") + ylab("Whatman No. 1 Filter Weight (grams)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
facet_wrap(~ Condition)
BiomassAndpH.metadata.ggplot.B
Supplementary figure 2 panel C
#ES5 by dilutions (side by side)
BiomassAndpH.metadata.pH.ggplot.C <- ggplot(BiomassAndpH.metadata,
aes(x = reorder(Isolate, +pH),
y = pH, fill=pHMeasurement)) +
geom_boxplot() +
scale_fill_grey(start =0.4, end = 1) + labs(tag = "C") +
xlab("Treatment") + ylab("pH") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
facet_wrap(~ Condition)
BiomassAndpH.metadata.pH.ggplot.C
Supplementary figure 2 panel D
ES8.chem <- read.csv("../raw_data/ES8_chem.chl.csv", header = T)
ES8.digital <- read.csv("../raw_data/ES8.digital.chl.csv", header = T)
ES8.digital.sum <-aggregate(ES8.digital$chl,list(ES8.digital$ExpCode),sum)
names(ES8.digital.sum)[names(ES8.digital.sum) == "x"] <- "dig.chl"
ES8.chem.dig = merge(ES8.chem, ES8.digital.sum, by.x='ExpCode', by.y='Group.1')
FinalpHvsChl.reg <- ggscatter(ES8.chem.dig, x = "dig.chl", y = "FinalpH",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Digital chlorophyll content (ng/sq mm)",
ylab = "Final pH") + labs(tag = "D")
FinalpHvsChl.reg## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing non-finite values (stat_cor).## Warning: Removed 11 rows containing missing values (geom_point).
Supplementary Figure 2 composite. Updated on 08/02/2021
gridExtra::grid.arrange(ES10.chem.dig.ggplot, BiomassAndpH.metadata.ggplot.B, BiomassAndpH.metadata.pH.ggplot.C, FinalpHvsChl.reg, ncol=2)## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing non-finite values (stat_cor).## Warning: Removed 11 rows containing missing values (geom_point).